diff --git a/src/deepdiagnostics/plots/lossPlot.py b/src/deepdiagnostics/plots/lossPlot.py
new file mode 100644
index 0000000..0974de1
--- /dev/null
+++ b/src/deepdiagnostics/plots/lossPlot.py
@@ -0,0 +1,55 @@
+import matplotlib.pyplot as plt
+from typing import Dict, List
+
+from deepdiagnostics.plots.plot import Display
+
+class LossPlot(Display): 
+    def __init__(self, model=None, data=None): 
+        ...
+
+    def _data_setup(self):
+        raise NotImplementedError
+
+    def plot_name(self):
+        return "loss_curves.png"
+   
+    def plot(
+       self,
+       training_history: Dict[str, List[float]],
+       epochs: int = None,
+       best_val_loss: float = None
+   ):
+        try:
+            training_loss_data = training_history["train_loss"]
+            validation_loss_data = training_history["val_loss"]
+        except Exception as e:
+            raise KeyError(f"Key {e} not found in supplied training history")
+
+        if epochs is None:
+            if len(training_loss_data) != len(validation_loss_data):
+                raise ValueError("Inconsistent training history data supplied [length of train and validation losses not equal]")
+            epochs = len(training_loss_data)
+            print(f"Number of epochs determined: {epochs}")
+        else:
+            if epochs != len(training_loss_data) or epochs != len(validation_loss_data):
+                raise ValueError("Epochs supplied inconsistent with training history data [epochs and loss history not equal]")
+ 
+        if best_val_loss is None:
+            best_val_loss = min(validation_loss_data)
+            print(f"Best validation loss found: {best_val_loss}")
+
+        epochs_trained = [ x for x in range(1, epochs+1) ]
+        plt.plot(epochs_trained, training_loss_data, label='Training Loss')
+        plt.plot(epochs_trained, validation_loss_data, label='Validation Loss')
+        plt.axhline(y=best_val_loss, color='m', linestyle='--', label="Best Val. Loss")
+
+        plt.xlabel('Epochs')
+        plt.ylabel('Loss')
+        plt.title('Training and Validation Loss Over Epochs')
+
+        plt.legend()
+        plt.grid()
+        plt.show()
+    
+    def __call__(self, **kwargs):
+        raise NotImplementedError("Plotting loss is not supported in pipeline mode")