The　main　purpose　of　this　study　is　to　observe　the　importance　of　machine　vision　(MV)　approach　for　the　identification　of　five　types　of　skin　cancers,　namely,　actinic-keratosis,　benign,　solar-lentigo,　malignant,　and　nevus.　The　1000　(200　x　5)　benchmark　image　datasets　of　skin　cancers　are　collected　from　the　International　Skin　Imaging　Collaboration　(ISIC).　The　acquired　ISIC　image　datasets　were　transformed　into　texture　feature　dataset　that　was　a　combination　of　first-order　histogram　and　gray　level　co-occurrence　matrix　(GLCM)　features.　For　the　skin　cancer　image,　a　total　of　137,400　(229　x　3　x　200)　texture　features　were　acquired　on　three　nonover-lapping　regions　of　interest　(ROls).　Principal　component　analysis　(PCA)　clustering　approach　was　employed　for　reducing　the　dimension　of　feature　dataset.　Each　image　acquired　twenty　most　discriminate　features　based　on　two　different　approaches　of　statistical　features　such　as　average　correlation　coefficient　plus　probability　of　error　(ACC　+　POE)　and　Fisher　(Fis).　Furthermore,　a　correlation-based　feature　selection　(CFS)　approach　was　employed　for　feature　reduction,　and　optimized　12　features　were　acquired.　Furthermore,　a　classification　algorithm　naive　bayes　(NB),　Bayes　Net　(BN),　LMT　Tree,　and　multilayer　perception　(MLP)　using　10　K-fold　cross-validation　approach　were　employed　on　optimized　feature　datasets　and　the　overall　accuracy　achieved　by　MLP　is　97.1333%.