diff --git a/src/cnn.py b/src/cnn.py
new file mode 100644
index 0000000..a4eac83
--- /dev/null
+++ b/src/cnn.py
@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network (CNN) class.
+    """
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(7 * 7 * 64, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = x.view(-1, 7 * 7 * 64)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+def train_cnn(model, dataloader, epochs):
+    """
+    Function to train the CNN model.
+    """
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.CrossEntropyLoss()
+
+    for epoch in range(epochs):
+        for images, labels in dataloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()