Distributed generation (DG) sources have become an integral part of today's decentralized power systems. However, current DG systems are mostly passive and do not provide intelligent information to help detect power quality issues. In this paper, a novel and intelligent event classi cation scheme is proposed to provide the DG systems with real-time decision making capabilities. The proposed technique has the ability to provide information to help maintain the quality and reliability of the DG systems under various disturbances or operating conditions. This event classi cation technique was developed using arti cial neural networks (ANNs) with a pre-de ned set of local input parameters. The algorithm is implemented using four parallel ANNs that were designed to operate under a majority vote fusion algorithm representing the nal classi cation output. A total of 310 event cases were generated to test the performance of the proposed technique. Simulation results showed that events were accurately classi ed within 10 cycles of their occurrences while achieving a 96.21% average classi cation accuracy.