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.