A general approach to the use of neural networks for data fusion is outlined. The discussion begins with examples of data fusion problems and a pattern recognition example is given to illustrate the concepts involved in data fusion. The differences between using post- and pre-detection signals and the advantages of using the latter are discussed. How to apply a neural network to the data fusion problem is demonstrated, and experimental results for a character recognition task are given. The general approach applies to a variety of practical situations, including robot navigation and military environment assessment/evaluation. >