In recent decades, the behavior of Caenorhabditis elegans (C. elegans) has been extensively studied to understand the respective roles of neural control and biomechanics. Thus far, however, only a few studies on the simulation modeling of C. elegans swimming behavior have been conducted because it is mathematically difficult to describe its complicated behavior. In this study, we built two hidden Markov models (HMMs), corresponding to the ments of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 ppm), respectively. The ment was characterized by a series of shape patterns of the organism, taken every 0.25 s for 40 min. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into seven patterns by using the self-organizing map (SOM) and the k-means clustering algorithm. The HMM coupled with the SOM was successful in accurately explaining the organism’s behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems for real-time applications to determine water quality.