This paper describes progress to date in the development of a system for representing various forms of real-world knowledge. The knowledge is stored in the form of a net of simple parallel processing elements, which allow certain types of deduction and set-intersection to be performed very quickly and easily. It is claimed that this approach offers definite advantages for recognition and many other data-accessing tasks. Suggestions are included for the application of this system as a tool in vision, natural-language processing, speech recognition, and other problem domains.
