(Krishnamurthy et al. 1993) studied one type of Hidden Markov Model (HMM) with identifying its state sequence and parameters based on the Expectation-Maximization (EM) algorithm, thus requiring extensive computing resources and a prior knowledge of state number. In this paper, we further study this model and present a new identification approach, which estimates the state sequence and HMM parameters through using the clustering information obtained via Rival Penalized Competitive Learning (RPCL) algorithm (Xu et al., 1992, 1993). Compared to Krishnamurthy's method, our approach cannot only fast identify the HMM, but also automatically find out the correct number of states. Experiments have successfully shown the performance of this approach.