Electrical and mechanical failures (such as bearing, winding and rotating-diode failures) combine to cause premature failures of the generators, which become a flight safety issue forcing the crew to land as soon as practical. Currently, diagnostic / prognostic technologies are not implemented for aircraft generators where repairs are time-consuming and costly. This paper presents the development of feature extraction and diagnostic algorithms to 1) differentiate between these failure modes and normal aircraft operational modes; and 2) determine the degree of damage of a generator. Electrical signature analysis (ESA) based time-domain features were developed to distinguish between healthy and degraded generators while taking into account their operating conditions. Frequency-domain based ESA techniques are used to identify the degraded components within the generators. The diagnostic algorithms were developed to have a high fault / high-hour detection rate along with a low false alarm rate. The feature extraction and diagnostic algorithms were evaluated against P-3 generator data (phase voltages/currents, exciter current) collected at various loads and operating line frequencies for the following: 1) healthy, low-hour and high-hour generators; and 2) a generator undergoing endurance testing. The results show that the electrical signature analysis of the generator's phase voltages can be used to detect and track its health. Work is in progress to develop and validate diagnostic and prognostic algorithms for electrical generators attached to linear and nonlinear loads for various operating conditions.