In order to create a method to monitor the structural integrity of aerospace systems that can utilize current technology, effectiveness of processing data produced by current instrumentation is desired. Utilizing modal vibration methods to measure the dynamic characteristics of a structure, an ANN's ability to discern patterns and then interpolate similar patterns from information it is unfamiliar with, creates an appropriate vehicle for developing a damage assessment system that performs, in a manner, with relatively low computational time. In this study, two ANN paradigms were utilized to create neural network systems to identify, quantify, and locate damage to an ideal three-degree-of-freedom system. Damage was defined as a percentage reduction in the properties of the elements comprising the three-degree-of-freedom system. An artificial neural network damage assessment system based on the back- propagation paradigm was created and then compared against an artificial neural network damage assessment system based on the radial basis function paradigm. Both systems utilized the same data, consisting of resonant frequencies and modes of vibration, to evaluate the condition of all the elements of the three-degree-of-freedom system. Results show that the radial basis function network performed with a greater efficacy and robustness in assessing damage for this system.