diff --git a/gymnasium/wrappers/resize_observation.py b/gymnasium/wrappers/resize_observation.py
index 034a2a337..2f8d2b664 100644
--- a/gymnasium/wrappers/resize_observation.py
+++ b/gymnasium/wrappers/resize_observation.py
@@ -1,5 +1,5 @@
 """Wrapper for resizing observations."""
-from typing import Union
+from __future__ import annotations
 
 import numpy as np
 
@@ -11,9 +11,12 @@
 class ResizeObservation(gym.ObservationWrapper):
     """Resize the image observation.
 
-    This wrapper works on environments with image observations (or more generally observations of shape AxBxC) and resizes
-    the observation to the shape given by the 2-tuple :attr:`shape`. The argument :attr:`shape` may also be an integer.
-    In that case, the observation is scaled to a square of side-length :attr:`shape`.
+    This wrapper works on environments with image observations. More generally,
+    the input can either be two-dimensional (AxB, e.g. grayscale images) or
+    three-dimensional (AxBxC, e.g. color images). This resizes the observation
+    to the shape given by the 2-tuple :attr:`shape`.
+    The argument :attr:`shape` may also be an integer, in which case, the
+    observation is scaled to a square of side-length :attr:`shape`.
 
     Example:
         >>> import gymnasium as gym
@@ -25,7 +28,7 @@ class ResizeObservation(gym.ObservationWrapper):
         (64, 64, 3)
     """
 
-    def __init__(self, env: gym.Env, shape: Union[tuple, int]):
+    def __init__(self, env: gym.Env, shape: tuple[int, int] | int) -> None:
         """Resizes image observations to shape given by :attr:`shape`.
 
         Args:
@@ -35,13 +38,22 @@ def __init__(self, env: gym.Env, shape: Union[tuple, int]):
         super().__init__(env)
         if isinstance(shape, int):
             shape = (shape, shape)
-        assert all(x > 0 for x in shape), shape
+        assert len(shape) == 2 and all(
+            x > 0 for x in shape
+        ), f"Expected shape to be a 2-tuple of positive integers, got: {shape}"
 
         self.shape = tuple(shape)
 
         assert isinstance(
             env.observation_space, Box
         ), f"Expected the observation space to be Box, actual type: {type(env.observation_space)}"
+        dims = len(env.observation_space.shape)
+        assert (
+            2 <= dims <= 3
+        ), f"Expected the observation space to have 2 or 3 dimensions, got: {dims}"
+
+        self.shape = tuple(shape)
+
         obs_shape = self.shape + env.observation_space.shape[2:]
         self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)
 
@@ -67,6 +79,4 @@ def observation(self, observation):
         observation = cv2.resize(
             observation, self.shape[::-1], interpolation=cv2.INTER_AREA
         )
-        if observation.ndim == 2:
-            observation = np.expand_dims(observation, -1)
-        return observation
+        return observation.reshape(self.observation_space.shape)
diff --git a/tests/wrappers/test_resize_observation.py b/tests/wrappers/test_resize_observation.py
index 1a264440b..ed0d587b7 100644
--- a/tests/wrappers/test_resize_observation.py
+++ b/tests/wrappers/test_resize_observation.py
@@ -2,14 +2,14 @@
 
 import gymnasium as gym
 from gymnasium import spaces
-from gymnasium.wrappers import ResizeObservation
+from gymnasium.wrappers import GrayScaleObservation, ResizeObservation
 
 
 @pytest.mark.parametrize("env_id", ["CarRacing-v2"])
 @pytest.mark.parametrize("shape", [16, 32, (8, 5), [10, 7]])
 def test_resize_observation(env_id, shape):
-    env = gym.make(env_id, disable_env_checker=True)
-    env = ResizeObservation(env, shape)
+    base_env = gym.make(env_id, disable_env_checker=True)
+    env = ResizeObservation(base_env, shape)
 
     assert isinstance(env.observation_space, spaces.Box)
     assert env.observation_space.shape[-1] == 3
@@ -20,3 +20,28 @@ def test_resize_observation(env_id, shape):
     else:
         assert env.observation_space.shape[:2] == tuple(shape)
         assert obs.shape == tuple(shape) + (3,)
+
+    # test two-dimensional input by grayscaling the observation
+    gray_env = GrayScaleObservation(base_env, keep_dim=False)
+    env = ResizeObservation(gray_env, shape)
+    obs, _ = env.reset()
+    if isinstance(shape, int):
+        assert env.observation_space.shape == obs.shape == (shape, shape)
+    else:
+        assert env.observation_space.shape == obs.shape == tuple(shape)
+
+
+def test_invalid_input():
+    env = gym.make("CarRacing-v2", disable_env_checker=True)
+    with pytest.raises(AssertionError):
+        ResizeObservation(env, ())
+    with pytest.raises(AssertionError):
+        ResizeObservation(env, (1,))
+    with pytest.raises(AssertionError):
+        ResizeObservation(env, (1, 1, 1, 1))
+    with pytest.raises(AssertionError):
+        ResizeObservation(env, -1)
+    with pytest.raises(AssertionError):
+        ResizeObservation(gym.make("CartPole-v1", disable_env_checker=True), 1)
+    with pytest.raises(AssertionError):
+        ResizeObservation(gym.make("Blackjack-v1", disable_env_checker=True), 1)