A　key　characteristic　of　biological　systems　is　the　ability　to　update　the　memory　by　learning　new　knowledge　and　removing　out-of-date　knowledge　so　that　intelligent　decision　can　be　made　based　on　the　relevant　knowledge　acquired　in　the　memory.　Inspired　by　this　fundamental　biological　principle,　this　article　proposes　a　multi-output　selective　ensemble　regression　(SER)　for　online　identification　of　multi-output　nonlinear　time-varying　industrial　processes.　Specifically,　an　adaptive　local　learning　approach　is　developed　to　automatically　identify　and　encode　a　newly　emerging　process　state　by　fitting　a　local　multi-output　linear　model　based　on　the　multi-output　hypothesis　testing.　This　growth　strategy　ensures　a　highly　diverse　and　independent　local　model　set.　The　online　modeling　is　constructed　as　a　multi-output　SER　predictor　by　optimizing　the　combining　weights　of　the　selected　local　multi-output　models　based　on　a　probability　metric.　An　effective　pruning　strategy　is　also　developed　to　remove　the　unwanted　out-of-date　local　multi-output　linear　models　in　order　to　achieve　low　online　computational　complexity　without　scarifying　the　prediction　accuracy.　A　simulated　two-output　process　and　two　real-world　identification　problems　are　used　to　demonstrate　the　effectiveness　of　the　proposed　multi-output　SER　over　a　range　of　benchmark　schemes　for　real-time　identification　of　multi-output　nonlinear　and　nonstationary　processes,　in　terms　of　both　online　identification　accuracy　and　computational　complexity.