Case-Based Reasoning (CBR), a well known Artificial Intelligence (AI) technique, has already proven its effectiveness in numerous industries. In this research, we try to adopt CBR technique in electroplating industry where the final products are electroplated accessory of watches. In order to ensure sufficient profit margin for electroplating manufacturer, it is important to grasp the coating weight of electroplating component accurately so that salespersons can make sure their quotation prices cover the precious metal cost. Apart from quotation accuracy, responsiveness is also a critical competitive edge in electroplating industry. In this connection, developing a quick response decision-making system with considerably reliable price is what electroplating industry needs. To cope with this problem, a hybrid CBR system combined with Rule-based Reasoning (RBR) and Fuzzy Logic (FL) concepts is established. Such system is capable to convert knowledge from experienced staff; simulate the ‘mind-set’ of decision maker in solving problem through acquisition of specific knowledge and experience; and build up self-learning characteristics. Moreover, this research interprets cases as some objective selection rules, putting CBR in a position much closer to RBR. This innovative concept differentiates from previous CBR researcher work, and will be explained through a practical example. Further, this research also suggested that it is very difficult and not practical to develop a pure CBR system. Applying some subjective guiding rules in CBR can significantly improve the performance of system in the early learning stage.