Traditional linear regression classification (LRC) suffers from a small sample size problem that the limited training samples of each class cannot comprehensively reflect different variations of the class. To address the problem, this paper proposes a novel iterative linear regression classification (ILRC) for image recognition. Different from traditional LRC, ILRC not only generates several new subspaces in each iteration but also uses the discrimination idea to optimize the training-set and testing samples. Extensive experiments on five benchmark databases demonstrate that the proposed ILRC classifier achieves better recognition performance than the traditional LRC and several state-of-the-art methods.