diff --git a/docs/guide/examples.rst b/docs/guide/examples.rst
index 5935f50..818f282 100644
--- a/docs/guide/examples.rst
+++ b/docs/guide/examples.rst
@@ -209,8 +209,8 @@ These dictionaries are randomly initialized on the creation of the environment a
   model.learn(total_timesteps=100_000)
 
 
-Using Callback: Monitoring Training
------------------------------------
+Callbacks: Monitoring Training
+------------------------------
 
 .. note::
 
@@ -308,6 +308,49 @@ If your callback returns False, training is aborted early.
   plt.show()
 
 
+Callbacks: Evaluate Agent Performance
+-------------------------------------
+To periodically evaluate an agent's performance on a separate test environment, use ``EvalCallback``.
+You can control the evaluation frequency with ``eval_freq`` to monitor your agent's progress during training.
+
+.. code-block:: python
+
+  import os
+  import gymnasium as gym
+
+  from stable_baselines3 import SAC
+  from stable_baselines3.common.callbacks import EvalCallback
+  from stable-baselines3.common.env_util import make_vec_env
+
+  env_id = "Pendulum-v1"
+  n_training_envs = 1
+  n_eval_envs = 5
+
+  # Create log dir where evaluation results will be saved
+  eval_log_dir = "./eval_logs/"
+  os.makedirs(eval_log_dir, exist_ok=True)
+
+  # Initialize a vectorized training environment with default parameters
+  train_env = make_vec_env(env_id, n_env=n_training_envs, seed=0)
+
+  # Separate evaluation env, with different parameters passed via env_kwargs
+  # Eval environments can be vectorized to speed up evaluation.
+  eval_env = make_vec_env(env_id, n_envs=n_eval_envs, seed=0,
+                          env_kwargs={'g':0.7})
+
+  # Create callback that evaluates agent for 5 episodes every 500 training environment steps.
+  # When using multiple training environments, agent will be evaluated every
+  # eval_freq calls to train_env.step(), thus it will be evaluated every
+  # (eval_freq * n_envs) training steps. See EvalCallback doc for more information.
+  eval_callback = EvalCallback(eval_env, best_model_save_path=eval_log_dir,
+                                log_path=eval_log_dir, eval_freq=max(500 // n_training_envs, 1),
+                                n_eval_episodes=5, deterministic=True,
+                                render=False)
+
+  model = SAC("MlpPolicy", train_env)
+  model.learn(5000, callback=eval_callback)
+
+
 Atari Games
 -----------
 
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 9492bd7..c08a923 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -61,6 +61,7 @@ Documentation:
 - Upgraded tutorials to Gymnasium API
 - Make it more explicit when using ``VecEnv`` vs Gym env
 - Added UAV_Navigation_DRL_AirSim to the project page (@heleidsn)
+- Added ``EvalCallback`` example (@sidney-tio)
 
 
 Release 1.8.0 (2023-04-07)