Abstract: Sparse representation uses only a few coefficients to linearly reconstruct the signal, which can avoid estimating the azimuth in synthetic aperture radar (SAR) target recognition as well as mitigate the impacts of strong coherent noises. The construction of the dictionary is a crucial issue in SAR target recognition under the framework of sparse representation. To improve the performance of SAR target recognition, the concatenated way and the parallel way are proposed to construct the dictionary for sparse representation. Firstly, the training samples are processed by the logarithmic transformation and then they are normalized. Moreover, principal components analysis (PCA) is employed to extract feature and reduce dimension. Secondly, the concatenated dictionary and the parallel dictionary are constructed, respectively. At last, the sparse representation of the SAR image is obtained by the truncated Newton interior-point method(TNIPM) with two different dictionaries, respectively. The testing sample belongs to the class with the minimum reconstruction error under the framework of the parallel dictionary while the class with the maximum coefficients sum under the framework of the concatenated dictionary. The experimental results demonstrate the effectiveness of our proposed algorithms.