Previous convolutional neural networks (CNNs) used for synthetic aperture radar (SAR) target recognition are over-parameterized which limits their application in real-time radar recognition systems. To solve this problem, a micro convolution neural network (MCNN) for SAR target recognition is proposed in this paper. Our MCNN is compressed from a deep convolutional neural network (DCNN) with 18 layers by a novel knowledge distillation algorithm. The experiments on MSTAR dataset show that the proposed MCNN can obtain the recognition rate of 98.2%. This recognition rate is almost the same as the DCNN. However, compared with the DCNN, the memory footprint of the proposed MCNN is compressed by nearly 177 times, and the calculated amount is nearly 12.8 times less, which means that the proposed MCNN can obtain a better performance with the smaller network.