diff --git a/artlib/common/BaseART.py b/artlib/common/BaseART.py
index 3af9e7f..6e6abce 100644
--- a/artlib/common/BaseART.py
+++ b/artlib/common/BaseART.py
@@ -646,6 +646,102 @@ def partial_fit(
             self.labels_[i + j] = c
         return self
 
+    def fit_gif(
+        self,
+        X: np.ndarray,
+        y: Optional[np.ndarray] = None,
+        match_reset_func: Optional[Callable] = None,
+        max_iter=1,
+        match_tracking: Literal["MT+", "MT-", "MT0", "MT1", "MT~"] = "MT+",
+        epsilon: float = 0.0,
+        verbose: bool = False,
+        ax: Optional[Axes] = None,
+        filename: Optional[str] = None,
+        colors: Optional[IndexableOrKeyable] = None,
+        n_cluster_estimate: int = 20,
+        fps: int = 5,
+        **kwargs,
+    ):
+        """Fit the model to the data and make a gif of the process.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            The dataset.
+        y : np.ndarray, optional
+            Not used. For compatibility.
+        match_reset_func : callable, optional
+            A callable that influences cluster creation.
+        max_iter : int, default=1
+            Number of iterations to fit the model on the same dataset.
+        match_tracking : {"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+"
+            Method for resetting match criterion.
+        epsilon : float, default=0.0
+            Epsilon value used for adjusting match criterion.
+        verbose : bool, default=False
+            If True, displays progress of the fitting process.
+        ax : matplotlib.axes.Axes, optional
+            Figure axes.
+        colors : IndexableOrKeyable, optional
+            Colors to use for each cluster.
+        n_cluster_estimate : int, default=20
+            estimate of number of clusters. Used for coloring plot.
+        fps : int, default=5
+            gif frames per second
+        **kwargs : dict
+            see :func: `artlib.common.BaseART.visualize`
+
+        """
+        import matplotlib.pyplot as plt
+        from matplotlib.animation import PillowWriter
+
+        if ax is None:
+            fig, ax = plt.subplots()
+            ax.set_xlim(-0.1, 1.1)
+            ax.set_ylim(-0.1, 1.1)
+        if filename is None:
+            filename = f"fit_gif_{self.__class__.__name__}.gif"
+        if colors is None:
+            from matplotlib.pyplot import cm
+
+            colors = cm.rainbow(np.linspace(0, 1, n_cluster_estimate))
+            black = np.array([[0, 0, 0, 1]])  # RGBA for black
+            colors = np.vstack((colors, black))  # Add black at the end
+
+        self.validate_data(X)
+        self.check_dimensions(X)
+        self.is_fitted_ = True
+
+        self.W = []
+        self.labels_ = -np.ones((X.shape[0],), dtype=int)
+
+        writer = PillowWriter(fps=fps)
+        with writer.saving(fig, filename, dpi=80):
+            for _ in range(max_iter):
+                if verbose:
+                    from tqdm import tqdm
+
+                    x_iter = tqdm(enumerate(X), total=int(X.shape[0]))
+                else:
+                    x_iter = enumerate(X)
+                for i, x in x_iter:
+                    self.pre_step_fit(X)
+                    c = self.step_fit(
+                        x,
+                        match_reset_func=match_reset_func,
+                        match_tracking=match_tracking,
+                        epsilon=epsilon,
+                    )
+                    self.labels_[i] = c
+                    self.post_step_fit(X)
+                    ax.clear()
+                    ax.set_xlim(-0.1, 1.1)
+                    ax.set_ylim(-0.1, 1.1)
+                    self.visualize(X, self.labels_, ax, colors=colors, **kwargs)
+                    writer.grab_frame()
+            self.post_fit(X)
+            return self
+
     def predict(self, X: np.ndarray) -> np.ndarray:
         """Predict labels for the data.
 
diff --git a/examples/demo_make_gif.py b/examples/demo_make_gif.py
new file mode 100644
index 0000000..687e4a0
--- /dev/null
+++ b/examples/demo_make_gif.py
@@ -0,0 +1,33 @@
+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+
+from artlib import FuzzyART
+
+
+def cluster_blobs():
+    data, target = make_blobs(
+        n_samples=150,
+        centers=3,
+        cluster_std=0.50,
+        random_state=0,
+        shuffle=False,
+    )
+    print("Data has shape:", data.shape)
+
+    params = {"rho": 0.5, "alpha": 0.0, "beta": 1.0}
+    cls = FuzzyART(**params)
+
+    X = cls.prepare_data(data)
+    print("Prepared data has shape:", X.shape)
+
+    cls = cls.fit_gif(X, filename="fit_gif_FuzzyART.gif", n_cluster_estimate=3)
+    y = cls.labels_
+
+    print(f"{cls.n_clusters} clusters found")
+
+    cls.visualize(X, y)
+    plt.show()
+
+
+if __name__ == "__main__":
+    cluster_blobs()