To　improve　precision　of　traditional　texture　image　classify　algorithm,　a　new　texture　image　classification　method　based　on　belief　rule-base　inference　methodology　using　evidential　reasoning　approach(RIMER)　is　proposed.　Researches　on　texture　image　classification　generally　consider　improving　texture　feature　extraction,　and　the　design　of　classifier　that　is　crucial　to　classification　precision　is　largely　ignored.　In　this　paper,　a　rule-base　inference　method　using　an　evidential　reasoning　approach　is　proposed.　The　classifier　is　redesigned　based　on　the　current　methods　of　texture　feature　extraction.　Algorithms　of　angular-radialtransform　and　gray-level　con-occurrence　matrix　are　used　to　extract　texture　image　feature.　Principle　component　analysis　is　carried　out　to　solve　the　problem　that　the　size　of　a　belief　rule　base(BRB)　classifier　is　controlled　within　a　feasible　range.　The　approach　of　rule-base　inference　method　with　evidential　reasoning　transforms　the　texture　features　into　classified　belief　degree　information.　Practicability　and　effectiveness　of　the　proposed　approach　is　validated　in　a　case　study.　©　2017,　Editorial　Office　of　Journal　of　Applied　Sciences.　All　right　reserved.