The　hidden　Markov　model　(HMM)　is　a　broadly　applied　generative　model　for　representing　time-series　data,　and　clustering　HMMs　attract　increased　interest　from　machine　learning　researchers.　However,　the　number　of　clusters　(K)　and　the　number　of　hidden　states　(S)　for　cluster　centers　are　still　difficult　to　determine.　In　this　article,　we　propose　a　novel　HMM-based　clustering　algorithm,　the　variational　Bayesian　hierarchical　EM　algorithm,　which　clusters　HMMs　through　their　densities　and　priors　and　simultaneously　learns　posteriors　for　the　novel　HMM　cluster　centers　that　compactly　represent　the　structure　of　each　cluster.　The　numbers　K　and　S　are　automatically　determined　in　two　ways.　First,　we　place　a　prior　on　the　pair　(K,S)　and　approximate　their　posterior　probabilities,　from　which　the　values　with　the　maximum　posterior　are　selected.　Second,　some　clusters　and　states　are　pruned　out　implicitly　when　no　data　samples　are　assigned　to　them,　thereby　leading　to　automatic　selection　of　the　model　complexity.　Experiments　on　synthetic　and　real　data　demonstrate　that　our　algorithm　performs　better　than　using　model　selection　techniques　with　maximum　likelihood　estimation.