Moving object detection and tracking (D&T) are important initial steps in object recognition, context analysis and indexing processes for visual surveillance systems. It is a big challenge for researchers to make a decision on which D&T algorithm is more suitable for which situation and/or environment and to determine how accurately object D&T (real-time or non-real-time) is made. There is a variety of object D&T algorithms (i.e. methods) and publications on their performance comparison and evaluation via performance metric. This paper discusses a hybrid technique for detecting and tracking moving pedestrians in a video sequence. The technique comprises two sub-systems: the first one for detecting and tracking moving objects in the visual field, and the second one for classifying the moving objects being tracked as human or cars by using MLP neural network. Experiments measuring the neural network s accuracy at classifying unseen computer generated and real moving objects are presented, along with potential applications of the technology.