There are specific characteristics in the thin film transistor liquid crystal display (TFT-LCD) industry, such as unexpected demand fluctuation, customized products that each customer will designate a specific key component, long lead time of procurement, and short product life cycle. This research presents a genetic algorithm integrated with a neural network model for monthly sales forecasting of TFT-LCD products in Taiwan. The practical situation and its requirements are explained and two systematic approaches are discussed: (a) a K-means clustering for historic sales data and new sales is forecasted by mapping its inputs into one of these clustered data, and (b) a neural network integrated with a genetic algorithm for supervised learning of the functional behaviour of time-series data and their approximation. The evolving neural network model is applied for modelling the system's behaviour with the possibility of exploiting expert information and systematic optimization. The model has been tested and satisfying results are shown with practical data.