The cutting tool industry has evolved over the last half century to the point where an increasing range and complexity of cutting tools are available for metal machining. This highlighted a need to provide an intelligent, user-friendly system of tool selection and recommendation that can also provide predictive economic performance data for engineers and end-users alike. Such an 'expert system' was developed for a local manufacturer of cutting tools in the form of a relational database to be accessed over the Internet. A number of performance predictive models were reviewed for various machining processes, however they did not encompass the wide range of variables encountered in metal machining, thus adaptation of these existing models for an expert system was reasoned to be economically prohibitive at this time. Interrogation of published expert systems from cutting tool manufacturers, showed the knowledge-engineered principle to be a common approach to transferring economic and technological information to an end-user. The key advantage being the flexibility to allow further improvements as new knowledge is gained. As such, a relational database was built upon the knowledge-engineered principle, based on skilled craft oriented knowledge to establish an expert system for selection and performance assessment of cutting tools. An investigation into tapping of austenitic stainless steels was undertaken to develop part of a larger expert system. The expert system was then interrogated in this specific area in order to challenge by experiment, the skilled craft oriented knowledge in this area. The experimental results were incorporated into the database where appropriate, providing a user-friendly working expert system for intelligent cutting tool selection, recommendation and performance data.