Online handwritten signature has been widely used for identity verification. However, it suffers from large intra-class variation problem as individual's signature may deviate from time to time due to variations in signing position, signature size, writing surface, and other factors. In addition, signatures are easier to forge than other biometrics and this leads to random and skilled forgeries issues. In this paper, we propose a novel Statistical Quantization Mechanism (SQM) to suppress the intra-class variation in signature features and thus discriminate the difference between genuine signature and its forgery. Experimental results show the proposed method is feasible in practice.