Global　land　cover　has　been　acknowledged　as　a　fundamental　variable　in　several　global-scale　studies　for　environment　and　climate　change.　Recent　developments　in　global　land-cover　mapping　focused　on　spatial　resolution　improvement　with　more　heterogeneous　features　to　integrate　the　spatial,　spectral,　and　temporal　information.　Although　the　high　dimensional　input　features　as　a　whole　lead　to　discriminatory　strengths　to　produce　more　accurate　land-cover　maps,　it　comes　at　the　cost　of　an　increased　classification　complexity.　The　feature　selection　method　has　become　a　necessity　for　dimensionality　reduction　in　classification　with　large　amounts　of　input　features.　In　this　study,　the　potential　of　feature　selection　in　global　land-cover　mapping　is　explored.　A　total　of　63　features　derived　from　the　Landsat　Thematic　Mapper　(TM)　spectral　bands,　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　time　series　enhanced　vegetation　index　(EVI)　data,　digital　elevation　model　(DEM),　and　many　climate-ecological　variables　and　global　training　samples　are　input　to　k-nearest　neighbours　(k-NN)　and　Random　Forest　(RF)　classifiers.　Two　filter　feature　selection　algorithms,　i.e.　Relieff　and　max-min-associated　(MNA),　were　employed　to　select　the　optimal　subsets　of　features　for　the　whole　world　and　different　biomes.　The　mapping　accuracies　with/without　feature　selection　were　evaluated　by　a　global　validation　sample　set.　Overall,　the　result　indicates　no　significant　accuracy　improvement　in　global　land-cover　mapping　after　dimensionality　reduction.　Nevertheless,　feature　selection　has　the　capability　of　identifying　useful　features　in　different　biomes　and　improves　the　computational　efficiency,　which　is　valuable　in　global-scale　computing.　©　2016　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.