A hidden Markov model (HMM) provides useful representations of dependent heterogeneous phenomena. So it becomes a popular method for modelling stochastic processes and time-dependent sequences, and is primarily applied in many different fields such as language, handwriting recognition, and molecular biology. Especially, in the sequence classification case, classification among known hidden Markov models is known to be accomplished with a classifier that minimizes the probability of error. In this paper, we first generate variables for the hidden state using the hidden markov model and then analyze the state using various classification methods. It differs from the existing analysis method by using the state variable and the mixture distribution based on the state rather than using the observed value directly in the analysis. In addition, it can be used to identify the relevance in the underlying process. As as illustration, we used the annual production of Matsutake mushroom data observed in five regions from 1997 to 2016.