Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields. Current metabonomic practice has relied on mass spectrometry (MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) to analyze metabolites. In this study, a strategy was developed for applying high-performance liquid chromatography (HPLC) and LC-MS-MS to metabonomics research. One of the key problems to be solved in this strategy is to match the peaks between the chromatograms. A peak alignment algorithm has been developed to match the chromatograms before the pattern recognition. As an application example, the strategy described above was applied to metabonomics research on liver diseases, and the false-positive result of live cancer diagnosis from the hepatocirrhosis and hepatitis diseases was effectively reduced to 7.40%. Based on the pattern recognition, several potential biomarkers were found and further identified by the following LC-MS-MS experiments. The structures of eight potential biomarkers were given for distinguishing the liver cancer from the hepatocirrhosis and hepatitis diseases.