In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for recognition of emotions from facial features. Local binary patterns have been proven to effectively describe the statistical characteristics of face image as it contains information related to edges, spots, etc. The aim of McFIS is to approximate the functional relationship between the facial features and various emotions. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: a) sample deletion b) sample learning and c) sample reserve. Performance of proposed McFIS based facial emotion recognition is evaluated on LBP features extracted from JAFFE database. The simulation results are compared with support vector machine classifier and other results available in literature. The results indicate the superior performance of McFIS in comparison to other algorithms.