Fuzzy Vision is an architecture being considered at Nasa for the interpretation of multiple successive images. Its goal is the development of a system that can accept a sequence of images from several sensors, and update an image interpretation fast enough to direct an autonomous vehicle. Fuzzy Vision is relatively noise insensitive, and can be mapped directly onto parallel procesing hardware using the Grundy  parallel processing system. Fuzzy Vision breaks the image interpretation problem into two distinct subsystems that access a semantic net  called the region structure. The sensors and a set of masks (arbitrary functions) comprise the first subsystem called the region generator. The region generator updates the values in the semantic net. The interconnectivity of the region structure is fixed, but the information stored at the nodes and links is altered. The second subystem is an expert system called the viewer that produces the object list which is the system output. The two subsystems operate asynchronously. The Fuzzy Visionarchitecture accepts several technologies and component implementations. The region generator can comprise slow scan cameras, ordinary cameras, radars, operator input, and other sensor input. Likewise, the viewer consists of several cooperating expert systems operating asynchronously. If noise is added to the picture, only the data in the region structure is altered, the stability of the viewer is not affected, and noise insensitivity is achieved. Since the region structure is specified, the viewer can be simpler than the expert systems usually used. Local autonomous navigation (under 10m) is too close for practical use of radar, and most laser techniques require cooperation from the target. Malfunctioning satellites or those built without reflectors cannot be easily serviced. A vision system that can interpret raster images into a list of objects in real time would allow autonomous astrogation in close quarters or perhaps on another satellite. A computer or remote operator can interrogate Fuzzy Vision, reducing bandwidth requirements by a factor of 1 million or more. There has been success with current vision understanding systems such as ACRONYM , but these systems, require large periods of time (hours) to analyze a simple image, will not accept data from other sources such as radar, and are often unstable in the presence of noise.