Hand and finger force data are used in many settings, including industrial design and indicating progress during rehabilitation. The application of appropriate work design principles, during the design of tools and workstations that involve the use of the hand and fingers, may minimize upper extremity injuries within the workplace. Determination and integration of force capabilities and requirements is an essential component of this process. Available data in the literature has focused primarily on whole-hand or multi-digit pinch exertions. The present study compiled and examined maximal forces exerted by the fingers in a variety of couplings to both enhance and supplement available data. This data was used to determine whether finger strength could be predicted from other strength measures and anthropometry. In addition, this study examined whether exerted finger forces could be estimated using surface electromyography obtained from standardized forearm locations. Such processes are of utility when designing and evaluating hand tools and human-machine interfaces involving finger intensive tasks, since the integration of finger force capabilities and task requirements are necessary to reduce the risk of injury to the upper limbs. Forces were measured using strain gauge transducers, and a modification of standard protocols was followed to obtain consistent and applicable data. Correlations within and among maximum finger forces, whole-hand grip force, and anthropometric measures were examined. Multiple regression models were developed to determine the feasibility of predicting of finger strength in various finger couplings from more accessible measures. After examining a wide variety of such mathematical models, the results suggest that finger strength can be predicted from easily obtained measures with only moderate accuracy (R²-adj: 0.45 - 0.64; standard error: 11.95N - 18.88N). Such models, however, begin to overcome the limitations of direct finger strength measurements of individuals. Surface electrodes were used to record electromyographic signals collected from three standardized electrode sites on the forearm. Multiple linear regression models were generated to predict finger force levels with the three normalized electromographic measures as predictor variables. The results suggest that standardized procedures for obtaining EMG data and simple linear models can be used to accurately predict finger forces (R²-adj: 0.77 - 0.88; standard error: 9.21N - 12.42N) during controlled maximal exertions. However, further work is needed to determine if the models can be generalized to more complex tasks. / Master of Science