Eastern gray squirrels produce alarm calls—vocalizations used in the presence of danger that influence the behavior of some receivers. This influence is possible because the alarm calls’ rate, duration, and structure contain information about the threat and the caller. Gray squirrels’ mix of different structural call types (kuks and quaas) could contain information on potential internal influences within the squirrel. Hidden Markov models (HMMs) are ideal tools to investigate whether hidden states explain the frequencies of kuks versus quaas throughout an alarm call sequence. In this study, we compare the ability of an iid (independent and identically distributed) model and two- to six-state HMMs to represent observed sequences of kukking, quaaing, and periods of silence. Audio recordings of 44 gray squirrels were collected and the first 30 s of each alarm call sequence was coded based on spectrograms. A number of HMMs were fitted, and the overall fit of the observed sequences to each model was assessed using Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), and Monte Carlo methods. The five-state HMM fit the observed call frequencies better than the other models, suggesting that the squirrels’ alarm calling sequences are influenced by a more complex temporal sequencing of acoustic units.