diff --git a/pina/plotter.py b/pina/plotter.py
index 6fb3af1..0d64afc 100644
--- a/pina/plotter.py
+++ b/pina/plotter.py
@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 import torch
 from pina.callbacks import MetricTracker
-
+from pina.utils import check_consistency
 from pina import LabelTensor
 
 
@@ -189,15 +189,19 @@ class Plotter:
         else:
             plt.show()
 
-    def plot_loss(self, trainer, metric=None, label=None, log_scale=True):
+    def plot_loss(self, trainer, metrics=None, logy = False, logx=False, filename=None, **kwargs):
         """
         Plot the loss function values during traininig.
 
         :param SolverInterface solver: the SolverInterface object.
-        :param str metric: the metric to use in the y axis.
-        :param str label: the label to use in the legend, defaults to None.
-        :param bool log_scale: If True, the y axis is in log scale. Default is
+        :param str/list(str) metric: The metrics to use in the y axis. If None, the mean loss
+            is plotted.
+        :param bool logy: If True, the y axis is in log scale. Default is
             True.
+        :param bool logx: If True, the x axis is in log scale. Default is
+            True.
+        :param str filename: the file name to save the plot. If None, the plot
+            is shown using the setted matplotlib frontend. Default is None.
         """
 
         # check that MetricTracker has been used
@@ -206,21 +210,34 @@ class Plotter:
             raise FileNotFoundError('MetricTracker should be used as a callback during training to'
                                     ' use this method.')
 
-        metrics = trainer.callbacks[list_[0]].metrics
+        # extract trainer metrics
+        trainer_metrics = trainer.callbacks[list_[0]].metrics
+        if metrics is None:
+            metrics = ['mean_loss']
+        elif not isinstance(metrics, list):
+            raise ValueError('metrics must be class list.')
+            
+        # loop over metrics to plot
+        for metric in metrics:
+            if metric not in trainer_metrics:
+                raise ValueError(f'{metric} not a valid metric. Available metrics are {list(trainer_metrics.keys())}.')
+            loss = trainer_metrics[metric]
+            epochs = range(len(loss))
+            plt.plot(epochs, loss, label=metric, **kwargs)
 
-        if not metric:
-            metric = 'mean_loss'
-
-        loss = metrics[metric]
-        epochs = range(len(loss))
-
-        if label is not None:
-            plt.plot(epochs, loss, label=label)
-            plt.legend()
-        else:
-            plt.plot(epochs, loss)
-
-        if log_scale:
-            plt.yscale('log')
+        # plotting
         plt.xlabel('epoch')
-        plt.ylabel(metric)
+        plt.ylabel('loss')
+        plt.legend()
+
+        # log axis
+        if logy:
+            plt.yscale('log')
+        if logx:
+            plt.xscale('log')
+
+        # saving in file
+        if filename:
+            plt.savefig(filename)
+        else:
+            plt.show()