Due to its synthetic and complex characteristics，the combustion process in the hematite ore-filled shaft furnace is noted for complex mechanism and frequent change of operating conditions，which results in frequent occurrence of faults and unsteady production. In order to reduce the faults ratio during the combustion process，an intelligent faults prediction approach was developed based on the combination of case-based reasoning（CBR）with soft-sensing. The soft-sensing model could estimate the key technical parameters which were difficult to measure online，and provide some information about the faults. Then，the fault prediction model based on case retrieval and reuse was adopted to make a thorough analysis on the combustion process. The model could provide the occurring probability of some typical faults，followed by corresponding operation instructions. The proposed fault prediction system was applied to the practical combustion process in a shaft furnace，and evidently eliminated the fault ratio.