Electric power distribution networks are exposed to the environment due to their length, for this reason, they are mostly affected by faults. These faults disrupt the continuous flow of power supply. There is also an associated loss of power that is generated that also determines the state of the economy. In order to reduce system downtime, it is necessary to integrate a system that detects and classifies faults quickly in order to hasten its clearance. This will bring about improvement in the efficiency and integrity of the power network. The artificial neural network as proposed in this study is meant to detect, classify and locate fault on the Rukpokwu 33-kV feeder of Port Harcourt Electricity Distribution Company (PHEDC). Fault detector, classifier and locator, with feed-forward back propagation were employed in the research. Matlab Simulink software was used to model and simulate the distribution network. The model was trained using s values of voltages and currents. With the simulation results, the efficiency of the proposed network was demonstrated for fault detection, classification and location. Mean square error (MSE) and confusion matrix were used to evaluate the performance of the proposed model. Results showed that the acceptable MSE of 0.00000027736 and an accuracy of 100% were achieved, which is satisfactory.