This　paper　proposed　an　intelligent　classification　method　for　multiple　time-scale　process.　Firstly,　the　fuzzy　production　rules　(FPRs)　are　established　to　describe　the　features　of　multiple　time-scale　process.　These　FPRs　can　cover　the　different　time-scale　by　endowing　with　local　and　global　weights　which　are　updated　following　the　changes　of　process.　Secondly,　a　fuzzy　min-max　network　(FMM)　with　enhanced　learning　algorithm　is　developed　for　classification　of　multiple　time-scale　process.　This　FMM　inserted　with　FPRs　can　handle　with　different　time-scale　inputs,　and　achieve　mature　parameters　of　FPRs　and　weights　using　enhanced　learning　algorithm.　Thirdly,　a　pruning　strategy　for　FMM　is　designed　to　can　deleted　the　useless　FPRs　during　the　learning　process,　so　as　to　improve　the　accuracy　of　its　classification.　Finally,　efficacy　of　intelligent　classification　method　is　evaluated　using　benchmark　data　sets　and　a　real　wastewater　biodegradability　evaluation　task.　The　results　show　that　the　proposed　method　is　better　than　those　various　FMM-based　models,　support　vector　machine-based,　Bayesian-based,　decision　tree-based,　fuzzy-based,　and　neural-based　classifiers.