This study focused on the development and assessment of the Medium Resolution Imaging Spectrometer(MERIS) land cover product. Four supervised classifiers including the Mahalanobis distance, maximum likelihood, decision trees and support vector machines (SVM) were applied to develop land covet’ information following the National Land Cover Database (NLCD) 2001 classification scheme. Results showed that SVM algorithm performed most optimally. The derived MERIS land cover was spatially close to NLCD 2001, although its capability for identifying ground details was less powerful than NLCD 2001. Furthermore, MERIS data were successful at delineating water, evergreen forest, barren land and cultivated crops, and less successful at characterizing deciduous forest and shrub/ scrub. Misclassification of shrub/scrub to barren land, evergreen forest, and grassland were observed in MERIS land cover. However, production of MERIS land cover is much less labor-intensive and cost-effective than that of NLCD2001, so the moderate resolution MERIS land cover may have value for specific applications. Future production of MERIS land cover should adequately use diverse ancillary information and a regionally tuned classification strategy to achieve more reliable results.