Tobacco leaf obtained from different geographical areas in China was profiled using gas chromatography-mass spectrometry (GC-MS) coupled with multivariate data analyses. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed that the tobacco metabolome was clearly dependent on geographical origins; climatic conditions, such as temperature and precipitation, imposed a greater impact on metabolite levels than the cultivars. By orthogonal partial least-squares-discrimination analysis (OPLS-DA), 20 metabolites that contributed to the discrimination were screened, including primary metabolites (sucrose, D-fructose, D-mannose, D-glucose, inositol, maleic acid, citric acid, malic acid, L-threonic acid, L-proline, L-phenylalanine), secondary metabolites (chlorogenic acid, α- and β-4,8,13-duvatriene-1,3-diol, nicotine, quinic acid), and four unknown metabolites. The results suggest that metabolic profiling using GC-MS combined with multivariate analysis can be used to discriminate tobacco leaf of different geographical origins and to provide potential indicators of tobacco origins.