In designing controllers for complex dynamical systems there are needs that are not sufficiently addressed by conventional control theory. These relate mainly to the problem of environmental uncertainty and often call for human-like decision making requiring the use of heuristic reasoning and learning experience. Learning is required complexity of a problem or the uncertainty thereof prevents a priori specification of a satisfactory solution. Such solutious are then only possible through accumulating information about the problem and using this information to dynamically generate an acceptable solution. Such systems can be referred to as intelligent control systems. In recent years, ’intelligent control’ has come to embrace diverse methodologies combining conventional control theory and emergent techniques based on physiological metaphors, such as neural networks, fuzzy logic, artificial intelligence, genetic algorithms and a wide variety of search and optimisation techniques. The paper reviews aspects of these emergent techniques, in particular, fuzzy logic, neural networks and genetic algorithms that pertain to realisation of intelligent control systems. The fundamental concepts and design techniques of each paradigm are discussed, providing a compact reference for their application.