The development of slag glass-ceramics has environmental & commercial value. However, new types of these materials are usually developed using the "trial & error" method because of little understanding of the relationship between the composition, processing, microstructure, & properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content & the properties of slag glass-ceramic. The investigation showed that the ANN model had an outstanding learning ability & was effective in complex data analysis. If the data set reflects the relationship of the composition & property, the trained network will learn the relationship & then give relatively accurate & stable prediction.
