Hidden Markov Models have been proved to be an efficient way for statistically modeling sequence signals. And the Support Vector Machines seem to be a promising candidate to perform the classification task. A new method combining support vector machine and hidden Markov models is proposed. The output of support vector machines are modified as posterior probability using sigmoid function, and act as a probability evaluator in the hidden states of HMM.