In the early sixties, AI researchers attempted to simulate the complicated process of thinking by finding general methods for solving broad classes of problems. This proved too difficult and such attempts failed. In the early seventies the problem was reformulated to include careful attention to data structures but the emphasis was still on general knowledge. Progress was still limited. In the late seventies the problem was further refined to focus almost completely on the knowledge representation. The goal was to make intelligent programs by providing them with high quality, domain-specific knowledge about some limited problem area. This strategy is much like that used by a human expert and gives rise to the term "expert system." What domains are appropriate for expert system work? First and foremost, for the present state of expert systems technology the problem domain must be of limited scope. A majority of the people within the application field must agree that real experts do exist. The problem must be knowledge, not data, intensive. A problem is knowledge intensive if there is substantial variability in people's ability to solve it. The problem must not require information from visual input. Multiple answers from the same input data can be handled but with limited success. Perhaps the best test of all for a potential candidate for expert system work is the so-called "telephone test." If you have a problem and you are confident that if you called some known expert in the field, he or she could solve the problem for you in 30 minutes or less over the phone, then the problem is likely to be amenable to an expert system solution. How do expert systems compare with human experts? The popular press has tended to be wildly optimistic about the present state of expert systems development. While many useful expert systems are available, they apply to very limited problem domains. In such domains expert systems can quickly provide answers that are consistent and objective. Expert systems can capture human expertise and make it permanent, widely available and easily portable. However, current expert systems lack the creativity and adaptability expected of a human expert. How do expert systems work? Regardless of the details of the implementation, an expert system is a program driven by an inference engine towards a specific goal. It is, in the limit, a remarkably simple