diff --git a/hiclass/LocalClassifierPerLevel.py b/hiclass/LocalClassifierPerLevel.py
index 907e61cf..896828cd 100644
--- a/hiclass/LocalClassifierPerLevel.py
+++ b/hiclass/LocalClassifierPerLevel.py
@@ -258,11 +258,14 @@ def _fit_classifier(self, level, separator):
             md5 = hashlib.md5(str(level).encode("utf-8")).hexdigest()
             filename = f"{self.tmp_dir}/{md5}.sav"
             if exists(filename):
-                (_, classifier) = pickle.load(open(filename, "rb"))
-                self.logger_.info(
-                    f"Loaded trained model for local classifier {level} from file {filename}"
-                )
-                return classifier
+                try:
+                    (_, classifier) = pickle.load(open(filename, "rb"))
+                    self.logger_.info(
+                        f"Loaded trained model for local classifier {level} from file {filename}"
+                    )
+                    return classifier
+                except (pickle.UnpicklingError, EOFError):
+                    self.logger_.error(f"Could not load model from file {filename}")
         self.logger_.info(f"Training local classifier {level}")
         X, y, sample_weight = self._remove_empty_leaves(
             separator, self.X_, self.y_[:, level], self.sample_weight_
diff --git a/hiclass/LocalClassifierPerNode.py b/hiclass/LocalClassifierPerNode.py
index 1382c72e..65c14113 100644
--- a/hiclass/LocalClassifierPerNode.py
+++ b/hiclass/LocalClassifierPerNode.py
@@ -249,12 +249,17 @@ def _fit_classifier(self, node):
             md5 = hashlib.md5(node.encode("utf-8")).hexdigest()
             filename = f"{self.tmp_dir}/{md5}.sav"
             if exists(filename):
-                (_, classifier) = pickle.load(open(filename, "rb"))
-                self.logger_.info(
-                    f"Loaded trained model for local classifier {node.split(self.separator_)[-1]} from file {filename}"
-                )
-                return classifier
-        self.logger_.info(f"Training local classifier {node}")
+                try:
+                    (_, classifier) = pickle.load(open(filename, "rb"))
+                    self.logger_.info(
+                        f"Loaded trained model for local classifier {node.split(self.separator_)[-1]} from file {filename}"
+                    )
+                    return classifier
+                except (pickle.UnpicklingError, EOFError):
+                    self.logger_.error(f"Could not load model from file {filename}")
+        self.logger_.info(
+            f"Training local classifier {str(node).split(self.separator_)[-1]}"
+        )
         X, y, sample_weight = self.binary_policy_.get_binary_examples(node)
         unique_y = np.unique(y)
         if len(unique_y) == 1 and self.replace_classifiers:
diff --git a/hiclass/LocalClassifierPerParentNode.py b/hiclass/LocalClassifierPerParentNode.py
index 47f77475..5873b52a 100644
--- a/hiclass/LocalClassifierPerParentNode.py
+++ b/hiclass/LocalClassifierPerParentNode.py
@@ -218,12 +218,17 @@ def _fit_classifier(self, node):
             md5 = hashlib.md5(node.encode("utf-8")).hexdigest()
             filename = f"{self.tmp_dir}/{md5}.sav"
             if exists(filename):
-                (_, classifier) = pickle.load(open(filename, "rb"))
-                self.logger_.info(
-                    f"Loaded trained model for local classifier {node.split(self.separator_)[-1]} from file {filename}"
-                )
-                return classifier
-        self.logger_.info(f"Training local classifier {node}")
+                try:
+                    (_, classifier) = pickle.load(open(filename, "rb"))
+                    self.logger_.info(
+                        f"Loaded trained model for local classifier {node.split(self.separator_)[-1]} from file {filename}"
+                    )
+                    return classifier
+                except (pickle.UnpicklingError, EOFError):
+                    self.logger_.error(f"Could not load model from file {filename}")
+        self.logger_.info(
+            f"Training local classifier {str(node).split(self.separator_)[-1]}"
+        )
         # get children examples
         X, y, sample_weight = self._get_successors(node)
         unique_y = np.unique(y)