In recent years, the form of production desired in mechanical manufacturing plants is shifting from mass production of a small number of products to small lot production of a variety of different products. Therefore, the realization of effective manufacturing systems suitable for this form of production has become an important desideratum. In this context, attention has been directed toward the solution of these requirement by using “autonomous machines”, i.e., equipment incorporating intelligent functions with a view to decentralized autonomous systems. However, in many cases, the systems so formed are ill-structured and the realization of highly complete “autonomous mechanical systems” at the very commencement of operation is difficult. That is, the machine must be a “mechanical systems with learning function”, possessing the capability of independently learning the pattern of dynamical changes in the operating environment, and updating or refining its own knowledge or operational characteristics accordingly. In the present article, an “autonomous mechanical systems with learning function”, capable of rapid and flexible event-driven or request-driven response to an instantaneously changing environment, is proposed. The basic structure of this type of machine is clarified, and, focussing the discussion upon grinding processes, the methodology of organizing grinding systems with learning function is described.