A synthetic aperture radar (SAR) target recognition was proposed using multi-layer projection feature based on 2D compressive sensing. 2D compressive sensing projection was employed as the basic feature extraction algorithm, which had the advantages of low dependency on the training samples, high efficiency, etc. Several projection matrices of 2D compressive sensing were constructed to extracted the multi-layer feature from original SAR images. The feature from different projection matrices had divergency, which reflected the gray distribution characteristics of SAR image from different aspect. Meanwhile, these feature came from the same input image, so they shared some inner correlation. Hence, the joint sparse representation was employed to classify the multi-layer feature, which could exploit their inner correlation to enhance the precision of each sparse representation problem. Finally, based on the solved sparse coefficients, the feature of the test sample was reconstructed on different training classes to obtain the reconstruction error. Based on the principle of the minimum reconstruction error, the target label of the test sample could be decided. The proposed method combined characteristics extraction of the multi-layer 2D compressive sensing and joint sparse representation classificaton to enhance the overall performance of SAR target recognition. The multi-class SAR images in the MSTAR dataset were used to test and validate the proposed method. The results confirm its reliable recognition performance under the standard operating condition(SOC) and extended operating conditions(EOC).