Before the infrared target is recognized by BP neural network,the recognition precision will be low and the structure of BP neural network will become complex if samples＇ data is not preprocessed and features are not extracted. In the paper, the principal componenet analysis is used to solve these problems.This method can extract main factors that explain the targets＇ sample and these factors are not correlative each other which can well satisfy the features optimization. The study result indicates that while the processed data is put into the neural networks,the precision of recognition is improved, the training time is reduced,and the structure of neural networks becomes simple.The innovation of the paper is two common methods are combined to recognize the infrared target. Firstly the principal component analysis is used to process the sample data,then the BP neural network is used to recognize the target.Compared with the traditional simple method,it improves the precision, furthermore reduces the calculation.