This research studies the process of acquiring knowledge from experts; that is, studies knowledge-acquisition methods to acquire expert knowledge. Forty subjects used a machine-aided knowledge-acquisition tool to model a word processing task. By using the tool, the subjects developed models that were on average 72.8% accurate with a baseline model of the task and 88.5 % consistent among themselves. This research makes four contributions: 1) a complete review of thirty-one knowledge-acquisition methods from manual to machine learning, 2) an evaluation methodology and metrics to evaluate knowledge-acquisition methods, 3) an evaluation of an automated knowledge-acquisition tool called Cognitive Analysis Tool (CAT) developed for this research, and 4) suggested improvements to the current version of the tool. This research describes, develops a taxonomy of, and evaluates thirty-one knowledge-acquisition methods to determine which method matches a defined set of criteria A method is chosen, extended, and automated in the form of a machine-aided knowledge-acquisition tool. The method is chosen based on five criteria including a connection between the chosen, method and the information processing model of problem solving as defined by Newell and Simon (1972). This research evaluates the performance of the tool in terms of the accuracy and consistency of the knowledge bases generated by using the tool. A baseline is derived from this study to which other knowledge-acquisition tools' performance can be compared. The evaluation methodology and metrics developed in this research can be used to evaluate other knowledge-acquisition tools. From this research, four groups of changes to the automated knowledge-acquisition tool are suggested to improve the usability and performance of the tool. The changes are suggested for the user interface and the modes of operation of the tool. / Master of Science