There are numerous advantages of distributed generation system over conventional generation systems, still protection has always been one major challenge in a distributed generation system. However, from the protection point of view, a distributed generator requires special attention on account of stability loss, failure re-closure, fluctuations in voltage, etc. The situation becomes even more challenging with a short circuit fault. And thereby, it becomes substantially more important to exactly locate and determine the fault type without delay, particularly for a small distributed generation system, which otherwise impacts the operation of the system. Several techniques, like the traveling wave methodology, impedance-based, genetic algorithm (GA),fuzzy logic, and support vector machine (SVM) had been discussed in the past to identify the type and location of a fault. However, the accuracy of all these methods has always been a major issue when incorporating them into a real system. The methodology proposed here uses a shallow artificial neural network-based structure that can be trained with real fault and steady-state data for locating and identifying the specific fault type. An elementary system containing two distributed generators and a utility grid has been considered for data recording purposes. Firstly, the training of the system through the recorded simulation data is carried out, followed by the validation and testing through an artificial neural network tool. After successful training and validation, the same data is tested for any given set of conditions. The modeling of the test system has been carried out in Simulink itself. The overall result shows an unprecedented zero percent error in identifying the type of fault.