The　simultaneous　quantitative　prediction　of　three　target　properties　(tensile　stress,　surface　hardness　and　case　depth)　in　medium　carbon　steel　rods　based　on　magnetic　nondestructive　testing　techniques　was　experimentally　investigated.　A　multifunctional　sensor　is　proposed　to　simultaneously　measure　the　magnetic　Barkhausen　noise,　tangential　magnetic　field　and　magnetic　hysteresis　curve　of　the　tensioned　steel　rods　with　different　depths　of　surface　hardened　layer.　Mean　impact　value　evaluation　algorithm　is　introduced　to　select　the　optimal　combination　from　the　sixteen　candidate　feature　parameters,　which　are　extracted　from　the　measured　signals,　as　the　input　nodes　of　BP　neural　network.　For　the　two　types　of　rods　of　45　steel　rods　and　42CrMo　steel,　the　established　BP　prediction　model　possesses　good　accuracy　in　simultaneous　quantitative　prediction　of　the　three　target　properties.　The　maximum　prediction　error　for　surface　hardness　is　less　than　1.6%.　The　average　prediction　error　for　tensile　stress　case　is　around　5.1%　in　45　steel　rods　and　5.3%　in　42CrMo　steel　rods.　Under　the　tensile　stress　higher　than　40　MPa,　the　average　prediction　error　is　less　than　3.4%.　Although　the　BP　prediction　model　may　lose　resolution　in　distinguishing　small　changes　in　the　case　depth　around　0.07　mm,　the　average　prediction　error　of　case　depth　in　both　types　of　medium　carbon　steel　rods　is　lower　than　5%.