Multiple sensing is the ability to sense the environment with the concurrent use of several sensors. There are currently a number of different sensors routinely used in image processing applications, and the trend is toward the development of more sophisticated and less expensive sensors. This trend is complemented by the development of parallel and multiprocessor architectures for processing the large amounts of data collected by these sensors. The capabilities of many image processing systems can be greatly enhanced by the organized use of several types of sensors and by the develop-ment of methods capable of integrating the collected data in a way that can yield information otherwise unavailable, or hard to obtain, from any single type of sensor. The advantage in using several similar sensors has already been demonstrated in the context of dynamic scene analysis, where information contained in a sequence of visual intensity images (including stereoscopic images) has been integrated with the twofold objective of obtaining the three-dimensional description and the motion of objects in space. The advantage in using several different sensors is clear from the quite obvious observation that different sensors are sensitive to different signals, each one of which can reveal a particular set of properties of the sensed environment. This paper discusses the advantages of multiple sensor integration/fusion with different sensors through image processing and identifies a number of associated problems. It reviews preliminary work on the solution of these problems and indicates the direction of future research.