Furnace　temperature　is　an　important　indicator　to　control　and　optimize　the　Municipal　Solid　Wastes　Incineration　(MSWI)　process.　However,　limited　by　the　environment　and　instruments,　it　is　difficult　to　measure　the　furnace　temperature　online　and　accurately.　In　this　paper,　a　TS　fuzzy　neural　network　is　utilized　to　design　the　prediction　model　in　MSWI　process,　trying　to　obtain　the　real-time　and　accurate　measurement　of　the　furnace　temperature.　First,　the　mechanism　of　the　MSWI　process　is　introduced　in　brief.　Then,　the　structure　and　training　method　of　the　TS-fuzzy-neural-network-based　prediction　model　is　introduced　in　details,　which　helps　to　build　the　nonlinear　relationship　between　the　furnace　temperature　and　other　process　variables.　Finally,　the　designed　prediction　model　is　applied　to　a　real　MSWI　plant,　and　simulation　results　demonstrate　the　effectiveness　and　outperformance　of　the　proposed　methodology.　©　2020　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.