Principal component analysis (PCA) is the well-known method in pattern recognition. However, expanding original image matrices into the same dimensional vectors in classical PCA increase the computational complexity. Here one presents a kind of multi-band principle component analysis (MBPCA). The process can reduce the computational complexity thus improving the overall performance. Firstly, the image is transformed into frequency data by the two-dimensional discrete cosine transform. Secondly, frequency data is divided into a plurality of frequency bands according to its frequency range. Finally, a principal component analysis method using a plurality of frequency bands is designed. The experiments on ORL and NUST603 face database show that the proposed method has the ability to quickly extract image features and performs better than the corresponding principal component analysis.