All new products are uncertain and risky by nature, Theseuncertainties affect the chance of success in new productdevelopment (NPD) leading to unsuccessfulcommercialization. One way to manage uncertainties andrisks in NPD process is to learn about the critical successfactors (CSF) and how they affect the success of newproduct development. This research is aimed at studyingthe relationship between the critical success factors and aset of performance measures in order to predict thechance of success in NPD by using the Artificial NeuralNetwork (ANN). ANN is a model of reasoning based onhuman brain. It was developed to solve problem withunknown pattern, insufficient or uncertain data byresembling the learning and working process of humanbrain. Input data to this research is obtained from asurvey of 57 companies in electronics industry. The datais categorized into two types: the companies employingNPD process, and those which do not have NPD processbut are capable of redesigning the products. The analysisis conducted by using the Feed-forward neural networkwith back propagation technique. The inputs consist of 36factors, each of which is a critical success factor in theNPD process. The outputs consist of 21 performancemeasures which follow the Balanced Scorecard’s fourperspectives: financial, customer, internal, and learning &growth. The preliminary findings show that ANNtechnique has sufficient ability to predict the success ofnew product development in electronics industry.However, the accuracy of results is affected by a numberof factors such as the number of available data relative tothe variables of interest, the quality of data from thequestionaire.