Abstract: Treatment position recognition in medical images is a key technique in medical image processing. Due to the excellent performance of convolutional neural networks on features extraction and classification, an architecture of parallel convolutional neural networks is proposed to recognize treatment positions in X-ray images, which uses convolution kernels of different sizes to extract local features of different sizes in these images. The experimental analysis shows that parallel convolution neural networks, which can extract representative image features with more dimensions, are competent to classify and recognize treatment positions in medical images.