Classification of tea quality is now mainly performed according to the sensory results by professional tea tasters. However, this evaluation method is inconsistent in differentiating their qualities. A combination of a (1)H NMR technique and a multivariate analysis was introduced to the quality evaluation of green tea by means of a metabolomic technique. A broad range of metabolites were detected by (1)H NMR spectrometry. The principal component analysis (PCA) was used to reduce the complexity of the (1)H NMR spectra data set and provided the quality discrimination result. It offered an extensive clue for classification and quality assessment without any prepurification method. A set of green teas from a Japanese tea contest were analyzed by (1)H NMR to classify the quality with respect to that judged by tea tasters and to conceive a quality prediction model. Metabolic profiling and fingerprinting of (1)H NMR spectra of green teas with different quality were studied. PCA showed a separation between the high- and the low-quality green teas. The taste marker compounds contributing to the discrimination of tea quality were identified. Reliable prediction models were obtained by the partial least-squares projection to latent structure (PLS) analysis together with a preprocessing filter of both orthogonal signal correction (OSC) and a combination between OSC and wavelet transform algorithms.