In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing Al-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose. (C) 1997 Elsevier Science Limited.