In this paper, we propose a novel approach for recognizing facial expressions based on using an Active Appearance Model facial feature tracking system with the quadratic deformation model representations of facial expressions. Thirty seven Facial Feature points are tracked based on the MPEG-4 Facial Animation Parameters layout. The proposed approach relies on the Euclidean distance measures between the tracked feature points and the reference deformed facial feature points of the six main expressions (smile, sad, fear, disgust, surprise, and anger). An evaluation of 30 model subjects, selected randomly from the Cohn-Kanade Database, was carried out. Results show that the main six facial expressions can successfully be recognized with an overall recognition accuracy of 89%. The proposed approach yields to promising recognition rates and can be used in real time applications.