In this study we explored the ability of ensembles of decision trees to classify hand-to-mouth gestures in order to detect cigarette smoke inhalations. Three subject independent models were constructed using a variety of ensemble techniques: boosting (AdaBoost), bootstrap aggregating (bagging), and Random Forests. Data was gathered during previous studies by extracting features from the signal waveforms of worn sensors. Each hand gesture was associated with either a smoke inhalation or a hand gesture of another type (e.g. eating). Subject as well as group models were trained. For the group models, model performance was evaluated by computing F-score, precision, and recall statistics using a 20-fold leave-one-out cross-validation testing strategy where one subject was held out for evaluation and models were trained on the remaining 19 subjects. For the individual models, models were trained on a single subject and evaluated using 5-fold cross validation. The average and standard deviation of each statistic across all folds were reported.