Linguistic summaries are an intuitive tool for obtaining analysis and data mining results that are easy to use, even for novice users. Until now, linguistic summarization has been used primarily to describe and facilitate the interpretation of large data sets. This work aims to develop methods enabling the construction of linguistically quantified sentences reflecting both the sequence of observations of a time series as well as the estimated parameters of hidden Markov models. The resulting fuzzy linguistic summaries with hidden Markov models (HMMs) may be exemplified as follows: “For most observations around 1.1, we have a high exact match rate”. Preliminary results illustrate the effectiveness of the proposed approach using simulation methods.