Abstract: Automatic facial expression analysis is an interesting and challenging problem and impacts important applications in many areas such as human–computer interaction. This study discusses the application of improved Active Appearance Model (AAM) based on evolutionary feature extraction in combination with Probabilistic Neural Network (PNN) for recognition of six different facial expressions from still pictures of the human face. Experimental results demonstrate an average expression recognition accuracy of 96% on the JAFFE database, which outperforms the rate of all other reported methods on the same database. The present study, therefore, proves the feasibility of computer vision based on facial expression recognition for practical applications like surveillance and human computer interaction.