diff --git a/README.md b/README.md
index fb1f1263..d7332aac 100644
--- a/README.md
+++ b/README.md
@@ -62,3 +62,9 @@ python train.py --train_manifest data/train_manifest.csv --val_manifest data/val
 ```
 
 Use `python train.py --help` for more parameters and options.
+
+There is also [Visdom](https://github.com/facebookresearch/visdom) support to visualise training. Once a server has been started, to use:
+
+```
+python train.py --visdom true
+```
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 888983eb..e9f686d5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 python-levenshtein
 librosa
-torch
\ No newline at end of file
+torch
+visdom
\ No newline at end of file
diff --git a/train.py b/train.py
index af71ef59..30805fb4 100644
--- a/train.py
+++ b/train.py
@@ -34,6 +34,7 @@
 parser.add_argument('--learning_anneal', default=1.1, type=float, help='Annealing applied to learning rate every epoch')
 parser.add_argument('--silent', default=False, type=bool, help='Turn off progress tracking per iteration')
 parser.add_argument('--epoch_save', default=False, type=bool, help='Save model every epoch')
+parser.add_argument('--visdom', default=False, type=bool, help='Turn on visdom graphing')
 parser.add_argument('--save_folder', default='models/', help='Location to save epoch models')
 parser.add_argument('--final_model_path', default='models/deepspeech_final.pth.tar',
                     help='Location to save final model')
@@ -72,6 +73,20 @@ def checkpoint(model, args, nout, epoch=None):
 def main():
     args = parser.parse_args()
     save_folder = args.save_folder
+
+    if args.visdom:
+        from visdom import Visdom
+        viz = Visdom()
+
+        opts = [dict(title='Loss', ylabel='Loss', xlabel='Epoch'),
+                dict(title='WER', ylabel='WER', xlabel='Epoch'),
+                dict(title='CER', ylabel='CER', xlabel='Epoch')]
+
+        viz_windows = [None, None, None]
+        loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
+            args.epochs)
+        epochs = torch.range(1, args.epochs)
+
     try:
         os.makedirs(save_folder)
     except OSError as e:
@@ -168,7 +183,7 @@ def main():
         avg_loss /= len(train_loader)
         print('Training Summary Epoch: [{0}]\t'
               'Average Loss {loss:.3f}\t'.format(
-            (epoch + 1), loss=avg_loss))
+            epoch + 1, loss=avg_loss))
 
         total_cer, total_wer = 0, 0
         for i, (data) in enumerate(test_loader):  # test
@@ -202,11 +217,35 @@ def main():
 
         wer = total_wer / len(test_loader.dataset)
         cer = total_cer / len(test_loader.dataset)
+        wer *= 100
+        cer *= 100
 
         print('Validation Summary Epoch: [{0}]\t'
               'Average WER {wer:.0f}\t'
               'Average CER {cer:.0f}\t'.format(
-            (epoch + 1), wer=wer * 100, cer=cer * 100))
+            epoch + 1, wer=wer, cer=cer))
+
+        if args.visdom:
+            loss_results[epoch] = avg_loss
+            wer_results[epoch] = wer
+            cer_results[epoch] = cer
+            epoch += 1
+            x_axis = epochs[0:epoch]
+            y_axis = [loss_results[0:epoch], wer_results[0:epoch], cer_results[0:epoch]]
+            for x in range(len(viz_windows)):
+                if viz_windows[x] is None:
+                    viz_windows[x] = viz.line(
+                        X=x_axis,
+                        Y=y_axis[x],
+                        opts=opts[x],
+                    )
+                else:
+                    viz.line(
+                        X=x_axis,
+                        Y=y_axis[x],
+                        win=viz_windows[x],
+                        update='replace',
+                    )
         if args.epoch_save:
             file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch)
             torch.save(checkpoint(model, args, len(labels), epoch), file_path)