In this paper, an efficient human action recognition system using feature points and single camera method based on neural network representation and recognition is proposed. By now, representation of action videos is based on learning rarely related human body posture method called Self Organizing Maps (SOM). Fuzzy distances from human body posture prototypes are used to produce a time invariant action representation. Multi layer perceptrons are used for action classification. The algorithm is trained using data from a multi-camera setup. An arbitrary number of cameras can be used in order to recognize actions using a Bayesian framework. Due to the growing interest in visual surveillance has led to human action recognition. So we propose a new and efficient method for human action recognition system using single camera and feature points. Our proposed method overcomes the problems in the existing system and recognizes the action of the required human. The system is developed in such a way, first it is trained using the feature extraction and feature tree method and then system will be capable of identifying the action from postures. We prove that our proposed is very efficient and can recognize actions quickly too.