Facial expressions are essential to recognize human emotions. This paper focuses on facial expression analysis based on hybrid approach using principal component analysis (PCA) and local binary patterns (LBP). In this paper, the data set contains six various face expressions that include a set of emotional expressions like anger, disgust, fear, happiness, neutral and surprise. It covers five elements like face detection; face part detection; localization of points; feature extraction and classification. The first step is executed by famous Viola-Jones algorithm. Active Shape Model technique is applied for locating feature point in certain area of the face. The feature extraction is performed using PCA and LBP technique. Finally the classification step is performed by multi-class support vector machine (SVM) classifier. PCA technique converts whole facial expression image into global gray scale features as well as reduces the data size. LBP technique is used to extract the texture of the specified region which is in the form of gray scale image. The recognition rate for PCA with multi-class SVM is found to be 67 % and accuracy is 42% whereas the recognition rate of 75% and accuracy of 75% are achieved after the inclusion of LBP. Hence, the proposed hybrid approach gives better recognition rate and accuracy in terms of recognizing facial emotions. © 2006-2018 Asian Research Publishing Network (ARPN).