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Nowadays, the telecommunication industries are facing substantial competition among the providers in order to capture new customers. Many providers have faced a loss of profitability due to the existing customers migrating to other providers. Customer retention program is one of the main strategies adopted in order to keep customers loyal to their provider. However, it requires a high cost and therefore the best strategy that companies could practice is to focus on identifying the customers that have the potential to churn at an early stage. The limited amount of research on investigating customer churn using machine learning techniques has lead this research to explore the potential of an artificial neural network to improve customer churn prediction. The research proposes Multilayer Perceptron (MLP) neural network approach to predict customer churn in one of the leading Malaysian’s telecommunication companies. The results are compared against the most popular churn prediction techniques such as Multiple Regression Analysis and Logistic Regression Analysis. The result has proven the supremacy of neural network (91.28% of prediction accuracy) over the statistical models in prediction tasks. Overall, the findings suggest that a neural network learning algorithm could offer a viable alternative to statistical predictive approaches in customer churn prediction.