The use of information technology is a vital part of everyday life, but for a person with functional impairments, technology interaction may be difficult at best. Information technology is commonly designed to meet the needs of a theoretical "normal" user. However, there is no such thing as a "normal" user. A user's capabilities will vary over time for a variety of reasons such as disease, developmental problems, age, injury, surgery, environmental conditions or cognitive status. Human computer interaction designers and researchers are attempting to integrate accessibility into the design of new technologies. A fundamental challenge exists for this endeavor: assessing and describing varying levels of user capabilities to predict how effectively a person can interact with a particular type of information technology. Current practice is to use medical diagnoses or physical limitations as descriptors of functional capabilities. This approach is too imprecise and does not provide enough details about user abilities to be helpful. An alternative is user self-reporting or observation by another person, but these solutions rely on individual interpretations of capabilities and may introduce unwanted bias. This dissertation proposes a new objective, quantifiable, repeatable, and efficient methodology for assessing an individual's physical capabilities to use information technologies. Two functional assessment tools were developed based on a set of information technology oriented functional capabilities: a self-assessment survey and a computer administered performance-based functional assessment tool. In a proof-of-concept study, thirty-one users with a range of physical capabilities were evaluated using the proposed survey and performance-based techniques. Independent assessments served as the gold standard for comparison. Metrics identified through the performance-based assessment were compared to the gold standard and predictive models were generated via regression and correlation analysis. Initial models explained up to 92% of the variance in user capabilities. Through an iterative refinement process, observer focus and the resulting performance-based models were adjusted to produce a hybrid combined model. Of all the predictive models, the hybrid model explained the greatest amount of variance in user capabilities with the lowest error rate. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]