The Canonical Correlation Analysis (CCA) is employed to fuse the multi-resolution representations of Synthetic Aperture Radar (SAR) images, which is used for target recognition.The multi-resolution representations can provide hierarchical descriptions for the target characteristics, thus providing richer information for the following classification.In order to keep the correlations between multiple resolutions while reducing the redundancy, the Multiset Canonical Correlation Analysis (MCCA) is adopted to fuse them as a unified feature vector.The fused feature vector inherits the discriminability of different resolutions, which is beneficial to improving the effectiveness and efficiency of target recognition.The Sparse Representation-based Classification (SRC) is employed as the basic classifier to make decisions on the target labels.The performance evaluation of the proposed method is conducted on the public MSTAR dataset and the results confirm its validity.