Abstract: The emotional problems of students with autism may greatly affect their learning in e-learning environments. This paper presents the development of an emotion recognition mechanism based on a proposed emotional adjustment model for students with high-functioning autism in a mathematics e-learning environment. The physiological signals and facial expressions were obtained through evoking autistic students’ emotions in a mathematical e-learning environment, and used for training the emotion classification model and to verify the performance of the emotion classification mechanism. In total, 34 facial features were obtained experimentally that were conducted by using a counterbalanced design, and 46% of features were further extracted by the chi-square method, Information Gain (IG), and Wrapper feature selection methods. A Support Vector Machine was used to train the emotion recognition model and assess the performance of the proposed emotion recognition mechanism. Four emotional categori