The physical and chemical mechanisms in a refuse ncinerator are complex. It is difficult to make a full comprehension of the system without a thorough research and long-term on-site experiments. In addition, many sensors are equipped in refuse incineration plant and much data are collected, those data were supposed to be useful since there may be some operational experience within. But to cope with the huge data that may exceed the computation capability, sequential Forward Floating Search algorithm (SFFS) is used to reduce the data dimension and find relevant features as well as to remove redundant information. In this research, data mining technique is applied toward three critical target attributes, steam production, NOx and SOx, to build decision tree models and extract operational experiences in the form of decision rules. Those models are evaluated by predicting accuracies, and rules extracted from decision tree models are also of great help to the on-site operation and prediction as well.