Telecommunication sector has considerably large network infrastructure consisting of different network equipment to provide telecom services to the customers. As a service provider, telecom operators have to make sure that customers are getting services without interruption. To do so, they built network operation center to monitor the network status with the help of a fault management system (FMS). When malfunction or error appears on a piece of network equipment, they typically produce an alarm and transmits to the FMS, which is then shown to the monitoring team for trouble ticket creation. There is also a chance that failures that happened on one network element can cause other network elements to produce alarms as long as the network equipment’s are functionally dependent. Thus, during the trouble ticketing, the operators may need to collect further information such as relationship or dependency between alarms to ultimately distinguish the root source of the alarms. But the information on FMS lacks explanation and indication of the causes, forcing monitoring teams to manually figure out the cause and take action, which results in a poor decision making as well as a longer trouble shooting and service outage time. This study focuses on studying the association between network equipment alarms using a correlation analysis technique called Pearson correlation using history alarm data and studying the feasibility of using artificial neural network to build fault localization model to support the monitoring team with decision making. Fault localization models using one specific neural network architecture are built in different literatures. But in this study two neural network architectures namely, Feed forward neural network (FFNN) and cascade forward neural network (CFNN), are selected to build a model using the data of correlation analysis output, the microwave link topology and the history alarms as an input. Data preparation techniques such as data engineering and feature engineering have been applied to the collected alarm data from the existing fault management system. After building the models, they are evaluated using commonly used performance metrics such as accuracy and error measurement. Python is used as programming tool to perform the correlation analysis as well as to develop and compare the two neural network models. Experimentation results exhibit that CFNN achieves an accuracy of 97.7% while FFNN achieves 95.9% overall accuracy.