Recently, there has been increasing interest in computer-aided ergonomics and its applications, such as in the fields of intelligent robots, intelligent mobiles, intelligent stores, and so on. The operation of convenience stores (CVS) in Taiwan is facing a crossover revolution by providing multiple services, including daily fresh foods, a café, ticketing, and a grocery. Therefore, forecasting the daily sales of fresh foods is getting more and more complex due to the influence of both internal and external factors. Eventually, a reliable sales-forecasting system will play an important role in improving business strategies and increasing competitive advantages. The purpose of this study is the development of an enhanced hybrid sales-forecasting model of fresh foods, called ECFM (Enhanced Cluster and Forecast Model), for CVSs by combining a self-organization map (SOM) neural network and radial basis function (RBF) neural networks. The model is evaluated for a six-month sales data set of daily fresh foods at a chained CVS in Taiwan. Meanwhile, the performance of the proposed model is compared with that of fuzzy neural network (FNN) and cluster and forecast model (CFM). The result reveals that the proposed model is not only amenable but can also promise the fresh food sales forecasting for CVSs. © 2011 Wiley Periodicals, Inc.