A technique for designing and optimizing the next generation of smart process controllers has been developed in this dissertation. The literature review indicated that neural networks held the most promise for this application, yet fundamental limitations have prevented their introduction to commercial settings thus far. This fundamental limitation has been overcome through the enhancement of neural network theory. The approach taken in this research was to produce highly intelligent process control systems by accurately modeling the nervous structures of higher biological organisms. The mammalian cerebral cortex was selected as the primary model since it is the only computational element capable of interpreting and complex patterns that develop over time. However the choice of the mammalian cerebral cortex as the model introduced two new levels of network complexity. First, the cerebral cortex is a three dimensional structure with extremely complicated patterns of interconnectivity. Second, the structure of the cerebral cortex can only be realized when thousands or millions of neurons are integrated into a massive scale neural network. The neural networks developed in this research were designed around the Hebbian adaptation, the only training technique proven by the literature review to be applicable to massive scale networks. These design difficulties were resolved by not only modeling the cerebral cortex, but the process by which it develops and evolves in biological systems. To complete this model, an advanced genetic algorithm was produced, and a technique was developed to encode all functional and structural parameters that define the cerebral cortex into the artificial chromosome. The neural networks were designed by a cell growth simulation program that decoded the structural and functional information on the chromosome. The cell growth simulation program is capable of producing patterns of differentiation unique for any slight variations in the genetic parameters. These growth patterns are similar to patterns of cellular differentiation seen in biological systems. While the computational resources needed to implement a massive scale neural network are beyond that available in existing computer systems, the technique has produced output lists which fully define the interconnections and functional characteristic of the neurons, thereby laying the foundation for their future use in process control. / Ph. D.