Previous studies with the Personal Automatic Cigarette Tracker (PACT) wearable system have found that smoking presents a distinct temporal breathing pattern, which might be well-suited for recognition by hidden Markov models (HMMs). In this work, we explored the feasibility of using HMMs to characterize the temporal information of smoking inhalations contained in the respiratory signals such as tidal volume, airflow, and the signal from the hand-to-mouth proximity sensor. Left-to-right HMMs were built to classify smoking and non-smoking inhalations using either only the respiratory signals, or both respiratory and hand proximity signals. Using a data set of 20 subjects, a leave-one-out cross-validation was performed on each HMM. In the recognition of smoke inhalations, the highest average recall, precision and F-score perceived by the HMMs was 42.39%, 88.19% and 56.38%, respectively, providing a 7.3% improvement in recall against a previously reported Support Vector Machines.