Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking. The temporal information from every breathing segment was captured by fragmenting the tidal volume and airflow signals into smaller frames and computing features for each frame. These frames were used as observations to model the states of the HMMs through mixture of Gaussians. Using leave-one-out cross-validation, the classification performance showed an average precision, recall, and F-score of 60.37%, 67.01%, and 62.78%, respectively. Results suggest that respiratory signals can potentially be used as a primary or secondary source in the recognition of some human activities.