Accurate sitting posture recognition plays a crucial role in improving improper postures and reducing the risk of associated health issues. The inherent complexity of human behavior, however, poses a great challenge to the development of a practical sitting posture monitoring system with pressure sensors. Towards facilitating the use of features, choice of classification models, and way of evaluating a sitting posture recognizer, in this study a comparative study on pressure-sensor-based sitting posture monitoring is conducted. Specifically, we extract discriminant features from the sensor data based on the distribution of pressure sensors and explore different combinations of these features. Then, five commonly used classification models are evaluated towards building a robust sitting posture recognizer. Finally, extensive comparative experiments concerning four performance metrics are conducted on the collected datasets in