The　surface　hardness　is　important　index　for　the　manufacturing　quality　evaluation　of　ferromagnetic　materials　and　the　non-destructively　quantitative　testing　of　surface　hardness　is　one　of　the　hot　topics　in　the　field.　Magnetic　Barkhausen　noise　(MBN)　measurement　technique　is　applied　for　quantitative　evaluation　of　surface　hardness　in　S136　steel.　A　self-developed　device　is　used　to　conduct　MBN　measurements　in　sixty　specimens　of　different　surface　hardness.　The　coefficient　of　variation　of　the　repeated　measured　magnetic　parameters　is　analyzed　to　show　that　the　device　has　good　repeated　accuracy　for　MBN　measurements.　To　achieve　quantitative　characterization　of　the　surface　hardness　using　magnetic　parameters,　the　dependency　of　the　surface　hardness　on　six　parameters　extracted　from　the　measured　MBN　and　tangential　magnetic　field　signals　is　individually　analyzed.　More　importantly,　comparative　study　is　performed　among　the　simple　linear　regression　model,　multiple　linear　regression　model　and　BP　neural　network　model　to　evaluate　their　accuracy　in　surface　hardness　prediction.　The　results　show　that　the　BP　neural　network　model　employing　six　magnetic　parameters　as　inputs　has　highest　accuracy　in　surface　hardness　prediction.　A　total　of　300　cases　are　investigated　and　it　is　found　that　the　average　prediction　error　is　only　around　2.14%　and　the　maximum　error　is　about　11.74%,　and　the　prediction　errors　out　of　274　of　300　cases　are　less　than　5.00%.　A　reference　is　provided　for　realizing　the　nondestructive　quantitative　detection　of　steel　plate　surface　hardness　based　on　magnetic　Buckhausen　noise　method.　©　2019　Journal　of　Mechanical　Engineering.