To solve the problem of illumination variation in face recognition, we improve the traditional sparse representation classification (SRC) by using random projection, and propose an illumination robust face recognition method based on random projection and weighted residual of sparse representation based classification (RPWRSRC). By normalizing the illumination of the face image, the bad illumination on the face image is eliminated as much as possible. Then we introduce random projection into the proposed method. The face images with illumination correction are projected to multiple random spaces to enrich the illumination invariant features of the samples and to reduce the influence of illumination changes on face recognition. On this basis, we utilize the single residual classification to improve  the traditional sparse representation classification method. By multiple random projections and sparse representations, we can obtain multiple sample features and reconstructed residuals. With the energy of sample features, we determine the weights for fusion of respective reconstructed residual to get a more stable and reliable weighted residual instead of single residual in traditional SRC. Experiment results on Yale B and CMU PIE face databases show that, the proposed RPWRSRC has strong robustness for face recognition under different illumination conditions. Compared with the traditional SRC, its average recognition rates of the two experiments on Yale B face database are increased by 25.76% and 46.39% respectively, and the average recognition rate on CMU PIE is increased by about 10%.