We present an approach to recognizing characters in surface mount technology (SMT) product. An improved SMT product character recognition method is proposed which can obtain a good recognition rate. Some appropriate image processing algorithms, such as Gray processing, Low-pass Filter, Median Filter, and so on, are used to eliminate the noise. Then, Character image is obtained after character segmentation and character normalization. Finally, a three-layer back propagation (BP) neural network module is constructed. In order to improve the convergence rate of the network and avoid oscillation and divergence, the BP algorithm with momentum item is used. As a result, the SMT product character recognition system is developed. Experimental results indicate that the proposed character recognition can obtain satisfactory character-recognition rate and the recognition rate reached over by 98.6% when the hidden layer of BP neural network module has 20 nodes.