For a successful business, telecom companies need to provide consistent, quality and timely maintenance and support to their customers. This is difficult to achieve without timely availability of domain experts that can efficiently be managed using the Knowledge Base System (KBS). Different approaches are applied to implement KBS and researchers study accuracy performance of the approaches. However, there are limited performance studies on the approaches in the context of telecom companies and none for ethio telecom. Most of the studies are also only focused to accuracy performance of the approaches and other relevant performance metrics that have an impact on the incident handling time of Information Technology (IT) experts are not addressed well. This thesis work presents performance comparison for KBS development approaches in the context of operation support and maintenance process of ethio telecom. Rule-based, Case-based and Artificial Neural Network (ANN)-based KBS development approaches are considered and these approaches are modeled using data collected from ethio telecom technical support experts’ knowledge and experience through interviews and data analysis. For the performance analysis, data preprocessing techniques including data reduction, data cleaning, data integration and data transformation are applied on the collected datasets. Execution time, accuracy, F-measure, precision, recall, error rate and mean square error metrics that have an impact on incident handling time are used for the performance evaluation. Python programming language is used to model, train, evaluate and compare the selected approaches. Achieved performance results show that ANN-based KBS development approach has a better performance in terms of building and executing the model, taking a minimum time of 0.95 seconds. Furthermore, rule-based approach accomplishes the highest accuracy that is 98.6 %, with minimum error in identifying the class labels which are incident types. Based on the evaluation results on the execution time or response time, accuracy, and error measures which have an impact on the incident handling time, a hybrid KBS development approach from the ANN and rule-based KBS development approach can provide the best aggregate result.