This paper presents a new method to recognize human facial expression by feeding hybrid features to Self-organizing maps (SOM). Facial expression recognition is still a challenging problem that can be seen in many real applications such as security systems, behavioral science and clinical practices. In this work, we present a new way to analyze, represent and recognize human facial expressions from a video sequence. The proposed facial expression recognition framework comprises of following components: face detection, facial feature extraction and classification. Firstly, faces are detected based on skin color after removing background and noise effects from raw video sequences. Then each face image is aligned using vertex mask generation and the 1D transformation features are extracted to utilize the local information for each facial image. After reducing the dimension of features and doing independent component analysis, the new features are trained and tested using SOM. The experimental evaluation demonstrates the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos achieves superior recognition performance of 96.81% and 96.55% over state of the art methods.