Lower back pain is one of the leading causes for musculoskeletal disability throughout the world. A large percentage of the population suffers from lower back pain at some point in their life. One noninvasive approach to reduce back pain is postural modification which can be learned through training. In this context, wearables are becoming more and more prominent since they are capable of providing feedback about the user’s posture in real-time. Optimal, healthy posture depends on the position (sitting, standing, hinging) the user is in. Meaningful feedback needs to adapt to the current position and, in the best case, identify the position automatically to minimize necessary interactions from the user. In this work, we present results of classifying the positions of users based on the readings of the Gokhale SpineTracker device. We computed various features and evaluated the performance of K-Nearest Neighbors, Extra Trees, Artificial Neural Networks and AdaBoost for global inter-subject classification as well as for personalized subject specific classification.