Synthetic aperture radar (SAR) automatic target recognition (ATR) is an important support technology for modern battlefield intelligence reconnaissance and precision strikes. In order to improve the overall performance of SAR ATR, a method based on multiset canonical correlations analysis (MCCA) of two-dimensional (2D) projection features is proposed. First, a series of 2D random projection matrices are used to extract features from SAR images to obtain multi-level feature descriptions. Considering the correlation between these results and the possible redundancy and interference, they are further fused through MCCA to obtain a single feature vector. The sparse representation-based classification (SRC) is used to process the fusion feature vector to determine the target class. The experiment is carried out based on the MSTAR dataset to fully test the proposed methods. The experimental results verify its effectiveness.