Power and energy industry infrastructure dictates the economic growth in any country. The increased use of sophisticated sensitive ICT and semiconductor devices at homes and offices has also led to monitoring of voltage profile and has increased the challenges for utility and industry to focus on power quality related to voltage dips and swells. Industries where a delicate industrial processes demand a high quality voltage supply, such as textile, process industry or refinery can be particularly susceptible to problems with voltage dip because the systems are interconnected and a trip of any component in the process can cause the whole plant to shut down. The early detection and identification of voltage dip may help to improve classification and mitigation of voltage dip process, leading to a secure operation and reliable power system networks. In this context, this paper presents a novel technique for voltage dip detection in power networks with Distributed Generation (DG) using a simple feed forward Artificial Neural Network (ANN) with sigmoid hidden neuron. Voltage dip is generated through simulation in DIgSILENT Power Factory 14.0 software and the tests are carried out on IEEE 9-bus test system. The model is trained, tested and validated in Matlab environment using neural network Toolbox.