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diff --git a/neural_network.py b/neural_network.py
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--- /dev/null
+++ b/neural_network.py
@@ -0,0 +1,46 @@
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+def recognition():
+    mnist = tf.keras.datasets.mnist
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+    x_train = tf.keras.utils.normalize(x_train, axis=1)
+    x_test = tf.keras.utils.normalize(x_test, axis=1)
+
+    model = tf.keras.models.Sequential()
+    model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
+    model.add(tf.keras.layers.Dense(128, activation='relu'))
+    model.add(tf.keras.layers.Dense(128, activation='relu'))
+    model.add(tf.keras.layers.Dense(10, activation='softmax'))
+
+    model.compile(optimizer ='adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
+
+    model.fit(x_train, y_train, epochs = 3)
+    model.save('handwritten.model')
+
+    model = tf.keras.models.load_model('handwritten.model')
+
+    image_number = 1
+    while os.path.isfile(f"digits/digit{image_number}.png"):
+        try:
+            img = cv2.imread(f"digits/digit{image_number}.png")[:,:,0]
+            img = np.invert(np.array([img]))
+            prediction = model.predict(img)
+            print(f"This digit is probably a {np.argmax(prediction)}")
+            plt.imshow(img[0], cmap = plt.cm.binary)
+            plt.show()
+        except:
+            print("Error!")
+        finally:
+            image_number +=1
+
+    loss, accuracy = model.evaluate(x_test, y_test)
+
+    print(loss)
+    print(accuracy)
+
+recognition()
\ No newline at end of file