For improving classification accuracy,a novel fingerprint classification algorithm was proposed by combining the special capability of a singularity method and the Hidden Markov Model(HMM).The belief functions of the singularity classification and the HMM classification was assigned,respectively,then the combined belief function from the proposed method was determined by the Dempster-shafter(D-S).Finally, fingerprint classification was accomplished according to the classification criteria.The results show that the proposed method explores the effectiveness of singularity extraction and the capability of HMM in dealing with low-quality images in fingerprint classification.An experiment based on standard fingerprint datasets has verified that the classification accuracy reaches 94.5%,which indicates that the performance of the proposed algorithm is better than that of the singularity classification and HMM classification,respectively.