The role of Human posture recognition and the importance of emotion in the development and support of intelligent and social behaviour have gained increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. In this paper, a Classification approaches for human posture recognition system in video sequences is proposed. The system was trained and evaluated to classify five different human postures using both supervised and unsupervised learning classifiers. The supervised classifier used was Multilayer Perceptron Feedforward Neural Networks (MLP) whilst for unsupervised learning classifiers, Self Organizing Maps (SOM), Fuzzy C Means (FCM) and K Means have been employed. Results indicate that MLP performs (96% accuracy) much better than SOMs, FCM and K Means which give accuracies of 86%, 33% and 31% respectively However, the results show that supervised learning classifiers are superior to unsupervised ones for the task of human posture recognition

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