Application of fuzzy inference systems to automatic control was first reported in E. H. Mamdani’s paper, where a “fuzzy logic controller” was used to emulate a human operator’s control of a steam engine and boiler combination. Since then, “fuzzy logic control” has been recognized as the most significant and fruitful application for fuzzy logic. This chapter presents the basic concepts, notation, and basic learning algorithms for neural networks. It provides a brief review of the basic concepts of neural networks and the backpropagation learning algorithm. The chapter also presents a brief description of adaptive neuro-fuzzy systems. It also provides a brief review on the methods for neuro-fuzzy control. The chapter explores some remarks about adaptive control and model-based control. It describes the architectures and learning algorithms for adaptive networks, a unifying framework that subsumes almost all kinds of neural network paradigms with supervised learning capabilities. It reviews various approaches in adaptive neuro-control design.