Within the past decade and a half, there has been a renewed interest in two separate areas: bistatic or multistatic radar and artificial neural networks (ANNs). Multistatic radar systems offer many advantages over monostatic systems. One such advantage is better detection of objects with a low radar cross-section. ANNs are very useful for large scale processing or storing of data. In this paper, we study a combination of both multistatic radar and ANNs, for multiple target detection and tracking. For the detection phase, a basic bistatic radar geometry is used, with noise added to simulate a more realistic situation. To track the targets, a two layer, backpropagation ANN is used to process the data. At first, the network was used in two phases: the learning phase and then the recall phase. Although this provided good data near the training time, the network became easily confused when targets crossed. An adaptive feature has been added allowing the weights to be modified on line as new data becomes available, which means it learns continuously. Numerical results taken from tests on both circular and linear target paths are presented in this study.