The rapid development of computer vision, computational resources, artificial intelligence and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In minerals engineering numerous opportunities for the application of these systems exist, such as the characterization of flotation froth structures which is discussed in this paper by way of example. A general model for the development of feasible, real-time machine vision systems is proposed, which is based on an analogy with biological visual perception made possibly by a connectionist approach and the ability of neural networks to solve ill-posed problems. It is shown that both supervised and unsupervised neural nets can be used in different ways to analyze froth structures of industrial flotation cells. Unsupervised (self-organizing) neural nets can monitor process behaviors on a continuous rather than on a discrete basis, which makes the early detection of erratic process control possible. Since some losses in information are incurred with the use of self-organizing systems, intelligent monitoring and control systems would in practice probably be comprised of both types of neural nets.