In this paper, we propose an efficient algorithm for facial image recognition using multiple histogram features from spatial and frequency domains, respectively. In spatial domain, we utilize Local Binary Pattern (LBP) histogram due to its excellent robustness and strong discriminative power. In frequency domain, we utilize two types of histogram named binary vector quantization (BVQ) histogram and energy histogram extracted from low-frequency DCT domain. The former histogram feature is essential for utilizing the phase information of DCT coefficients by applying binary vector quantization (BVQ) on DCT coefficient blocks. The latter is energy histogram which can be considered to add magnitude information of DCT coefficients. These two histograms then contain both phase and magnitude information of a DCT transformed facial image. These 3 types of histograms described above, which contain both spatial and frequency domain information of a facial image, are utilized as a very effective personal feature. Publicly available AT&T database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. Experimental results demonstrated that face recognition using multiple histogram features can achieve higher recognition rate.