Abstract: Synthetic aperture radar (SAR) image interpretation is a major scientific application challenge. SAR image target recognition has become one of the main research directions in this field. In view of the fact that target recognition algorithms for SAR images needs many training parameters. This paper proposes a target recognition method based on the two-dimensional principal component analysis (2DPCA) and the L2 regularization constrained stochastic configuration network (SCN) for integrated learning. 2DPCA can not only effectively extract the feature information of the target but also reduce the amount of data through sparse representation. The Regularized SCN algorithm can effectively avoid the over-fitting problem and improve the recognition rate of the network. Based on the moving and stationary target acquisition and recognition (MSTAR) dataset, the experimental results show that the proposed method can achieve a higher recognition rate than the traditional method.