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In recent years, video surveillance and monitoring have gained importance because of security and safety concerns. Banks, borders, airports, stores, and parking areas are the important application areas. There are two main parts in scenario recognition: Low level processing, including moving object detection and object tracking, and feature extraction. We have developed new features through this work which are RUD (relative upper density), RMD (relative middle density) and RLD (relative lower density), and we have used other features such as aspect ratio, width, height, and color of the object. High level processing, including event start-end point detection, activity detection for each frame and scenario recognition for sequence of images. This part is the focus of our research, and different pattern recognition and classification methods are implemented and experimental results are analyzed. We looked into several methods of classification which are decision tree, frequency domain classification, neural network-based classification, Bayes classifier, and pattern recognition methods, which are control charts, and hidden Markov models. The control chart approach, which is a decision methodology, gives more promising results than other methodologies. Overlapping between events is one of the problems, hence we applied fuzzy logic technique to solve this problem. After using this method the total accuracy increased from 95.6 to 97.2.