Computational modeling of scientific discovery has been emer~/ng as an important research field in artificial intelligence. Building theoretical models for scientific development has until recently been the exclusive domain for philosophers of science. With the advances in artificial intelligence and especially in machine learning, opportunities have arisen for researchers in this field to test the learning methods developed in modeling scientific discovery. In the last fifteen years, a number of systems have been developed modeling various discoveries ranging from 17th to 20th century physics and chemistry. However, a methodology for building and evaluating such models has still not been developed. This paper focuses on the elements of historical discovery models, and the methods for their systematic construction and evaluation.