diff --git a/optika/__init__.py b/optika/__init__.py
index 4c12535..734f83c 100644
--- a/optika/__init__.py
+++ b/optika/__init__.py
@@ -1,3 +1,4 @@
+from . import vectors
 from . import rays
 from . import sags
 from . import materials
diff --git a/optika/vectors/__init__.py b/optika/vectors/__init__.py
index 6137332..9057ed9 100644
--- a/optika/vectors/__init__.py
+++ b/optika/vectors/__init__.py
@@ -1,3 +1,4 @@
 from ._vectors_field import *
 from ._vectors_pupil import *
 from ._vectors_scene import *
+from ._vectors_object import *
diff --git a/optika/vectors/_tests/test_vectors_object.py b/optika/vectors/_tests/test_vectors_object.py
new file mode 100644
index 0000000..dd7033a
--- /dev/null
+++ b/optika/vectors/_tests/test_vectors_object.py
@@ -0,0 +1,130 @@
+import pytest
+import numpy as np
+import astropy.units as u
+import named_arrays as na
+from named_arrays._vectors.cartesian.tests import test_vectors_cartesian
+import optika
+
+_num_y = test_vectors_cartesian._num_y
+
+
+vectors_object = [
+    optika.vectors.ObjectVectorArray(
+        wavelength=534 * u.nm,
+        field=na.Cartesian2dVectorLinearSpace(
+            start=-5 * u.mm,
+            stop=5 * u.mm,
+            axis="y",
+            num=_num_y,
+        ).explicit,
+    )
+]
+
+
+def _items() -> list[na.AbstractArray | dict[str, int | slice | na.AbstractArray]]:
+    return [
+        dict(y=0),
+        dict(y=slice(0, 1)),
+        dict(y=na.ScalarArrayRange(0, 2, axis="y")),
+    ]
+
+
+class AbstractTestAbstractObjectVectorArray(
+    test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray
+):
+    @pytest.mark.xfail(raises=NotImplementedError)
+    def test_matrix(self, array):
+        return super().test_matrix(array=array)
+
+    @pytest.mark.parametrize(
+        argnames="item",
+        argvalues=_items(),
+    )
+    def test__getitem__(
+        self,
+        array: optika.vectors.AbstractObjectVectorArray,
+        item: dict[str, int | slice | na.AbstractArray] | na.AbstractArray,
+    ):
+        super().test__getitem__(array=array, item=item)
+
+    @pytest.mark.parametrize("array_2", vectors_object)
+    class TestUfuncBinary(
+        test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestUfuncBinary
+    ):
+        pass
+
+    @pytest.mark.parametrize("array_2", vectors_object)
+    class TestMatmul(
+        test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestMatmul
+    ):
+        pass
+
+    class TestArrayFunctions(
+        test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestArrayFunctions
+    ):
+        @pytest.mark.parametrize("array_2", vectors_object)
+        class TestAsArrayLikeFunctions(
+            test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestArrayFunctions.TestAsArrayLikeFunctions
+        ):
+            pass
+
+        @pytest.mark.parametrize(
+            argnames="where",
+            argvalues=[
+                np._NoValue,
+            ],
+        )
+        class TestReductionFunctions(
+            test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestArrayFunctions.TestReductionFunctions,
+        ):
+            pass
+
+        @pytest.mark.parametrize(
+            argnames="q",
+            argvalues=[
+                25 * u.percent,
+            ],
+        )
+        class TestPercentileLikeFunctions(
+            test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestArrayFunctions.TestPercentileLikeFunctions,
+        ):
+            pass
+
+    class TestNamedArrayFunctions(
+        test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestNamedArrayFunctions,
+    ):
+        @pytest.mark.skip
+        class TestPltPlotLikeFunctions(
+            test_vectors_cartesian.AbstractTestAbstractCartesianVectorArray.TestNamedArrayFunctions.TestPltPlotLikeFunctions,
+        ):
+            pass
+
+
+@pytest.mark.parametrize("array", vectors_object)
+class TestObjectVectorArray(
+    AbstractTestAbstractObjectVectorArray,
+    test_vectors_cartesian.AbstractTestAbstractExplicitCartesianVectorArray,
+):
+    @pytest.mark.parametrize(
+        argnames="item",
+        argvalues=[
+            dict(y=0),
+            dict(y=slice(None)),
+        ],
+    )
+    @pytest.mark.parametrize(
+        argnames="value",
+        argvalues=[
+            optika.vectors.ObjectVectorArray(
+                wavelength=500 * u.nm,
+                field=0 * u.mm,
+            ),
+        ],
+    )
+    def test__setitem__(
+        self,
+        array: optika.vectors.AbstractObjectVectorArray,
+        item: dict[str, int | slice | na.ScalarArray] | na.ScalarArray,
+        value: optika.vectors.AbstractObjectVectorArray,
+    ):
+        super().test__setitem__(array=array, item=item, value=value)
diff --git a/optika/vectors/_vectors_object.py b/optika/vectors/_vectors_object.py
new file mode 100644
index 0000000..82cbfe3
--- /dev/null
+++ b/optika/vectors/_vectors_object.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+from typing import TypeVar
+from typing_extensions import Self
+import dataclasses
+import named_arrays as na
+from . import AbstractPupilVectorArray, PupilVectorArray
+from . import AbstractSceneVectorArray, SceneVectorArray
+
+
+WavelengthT = TypeVar("WavelengthT", bound=na.ScalarLike)
+FieldT = TypeVar("FieldT", bound=na.AbstractCartesian2dVectorArray)
+PupilT = TypeVar("PupilT", bound=na.AbstractCartesian2dVectorArray)
+
+
+@dataclasses.dataclass(eq=False, repr=False)
+class AbstractObjectVectorArray(
+    AbstractPupilVectorArray,
+    AbstractSceneVectorArray,
+):
+    @property
+    def type_abstract(self) -> type[AbstractObjectVectorArray]:
+        return AbstractObjectVectorArray
+
+    @property
+    def type_explicit(self) -> type[ObjectVectorArray]:
+        return ObjectVectorArray
+
+    @property
+    def type_matrix(self) -> type[na.AbstractMatrixArray]:
+        raise NotImplementedError
+
+
+@dataclasses.dataclass(eq=False, repr=False)
+class ObjectVectorArray(
+    AbstractObjectVectorArray,
+    PupilVectorArray,
+    SceneVectorArray,
+):
+    @classmethod
+    def from_scalar(
+        cls,
+        scalar: na.AbstractScalar,
+        like: None | Self = None,
+    ) -> Self:
+        if like is not None:
+            return type(like)(
+                wavelength=scalar,
+                field=scalar,
+                pupil=scalar,
+            )
+        else:
+            return cls(
+                wavelength=scalar,
+                field=scalar,
+                pupil=scalar,
+            )