In　traditional　methods　for　measuring　case　depths　(CDs)　in　two　types　of　surface-hardened　steels,　two　prediction　models　are　generally　established.　However,　it　is　difficult　to　recognize　material　types　(MTs)　of　surface-hardened　steels　based　on　the　appearance　of　steels,　thus　leading　to　difficulty　in　the　selection　of　an　appropriate　model　from　the　two　obtained　models　for　predicting　CD　in　a　given　surface-hardened　steel.　The　established　model　should　allow　both　the　prediction　of　the　CDs　in　two　types　of　surface-hardened　steels　and　MT　recognition.　In　this　study,　an　intelligent　approach　is　proposed　to　automatically　establish　a　prediction　model　for　simultaneously　performing　MT　recognition　and　CD　prediction　in　two　types　of　materials.　The　intelligent　approach　involves　sample　preparation,　magnetic　hysteresis　loop　(MHL)　measurements,　feature　generation,　feature　selection,　feature　extraction　and　prediction　model　establishment.　In　the　feature　generation　process,　the　entire　feature　set　is　generated　from　measured　MHL　signals.　In　the　feature　selection　process,　the　binary-bat-algorithm-based　(BBA)　feature　selection　is　firstly　repeated　50　times　to　select　feature　subsets　from　the　entire　feature　set.　Then,　a　threshold　criterion　is　proposed　to　extract　suitable　features　from　the　repeated　feature　selection　results　in　the　feature　extraction　step.　Finally,　a　modified　neural　network　is　proposed　for　predictions.　The　experimental　results　showed　that　the　extracted　features　could　give　richer　descriptions　of　the　MHL　signals,　as　well　as　the　properties　of　steels.　The　established　prediction　model　showed　good　performance　in　simultaneously　performing　MT　recognition　and　CD　prediction　in　two　types　of　materials.　The　prediction　error　of　case　depth　(PECD),　misclassification　rate　(MR)　and　test　time　were　1.39　x　10(-2)　mm　(3.47%),　0%　and　0.0105　s,　respectively,　demonstrating　that　the　proposed　approach　was　applicable　for　on-line　CD　measurements　in　two　types　of　surface-hardened　samples.