The　organic　Rankine　cycle　(ORC)　is　a　promising　technology　for　medium-and-low　temperature　heat　utilization.　However,　the　mechanism　of　how　system　parameters　affect　output　have　been　investigated　very　little　in　the　experimental　aspect.　Experimental　investigation　on　the　impact　of　each　system　parameter　on　system　performance　requires　decoupling　these　system　parameters.　In　this　work,　a　series　of　experiments　are　conducted　on　a　10　kW　scale　ORC　experiment　setup.　Statistical　analysis　is　performed　to　identify　a　key　parameter　subset　based　on　an　experimental　database.　6　system　parameters,　including　temperature　(Te)　and　pressure　(pe)　at　the　evaporator　outlet,　temperature　(Tc)　and　pressure　(pc)　at　the　condenser　inlet,　expander　shaft　efficiency　(eta　SSE),　and　working　fluid　pump　efficiency　(eta　P)　are　obtained.　Combined　with　the　ORC　net　power　output　and　thermal　efficiency,　an　experimental　database　of　system　operation　conditions　is　constructed.　Subsequently,　the　principal　component　analysis　(PCA)　of　ORC　is　conducted　based　on　the　experimental　database.　Prediction　models　are　developed　using　multi-linear　regression　(MLR),　back　propagation　artificial　neural　network　(BP-ANN),　and　support　vector　regression　(SVR).　Finally,　accounting　for　the　prediction　performance　of　models　and　system　parameter　intercorrelation　behavior,　the　key　parameter　subset　is　determined　with　the　exhaustive　feature　selection　method.　The　results　imply　that　the　key　parameter　subset　is　(pe,　eta　P,　pc,　eta　SSE).　Further　removing　or　including　more　system　parameters　would　reduce　the　accuracy　of　prediction　models.　In　addition,　the　MLR　models　are　slightly　less　accurate　than　the　more　sophisticated　BP-ANN　and　SVR　models.