The development of robust automatic control systems has proved difficult because of the complexity of the problem. Flotation is notorious for its susceptibility to process upsets and consequently its poor performance, making successful flotation control systems an elusive goal. Machine vision systems provide a novel solution to several of the problems encountered in conventional flotation systems for monitoring and control. In previous work powerful techniques have been developed for the extraction of flotation froth appearance features such as average bubble size, froth mobility and stability, chromatic information and textural properties of surface froth. A methodology has been developed for the classification of froths, based on appearance and metallurgical significance. The objective of this paper is to provide a clear framework and motivation for the development of a machine vision system for flotation control. A systematic discussion of the diffuse literature descriptions about the relation between froth appearance and fundamental flotation principles is presented. A preliminary classification strategy for flotation froths is proposed and an example of how process deviations can be related to froth appearance is provided. Design constraints and principles imposed on a vision system by flotation are also discussed.