Ankylosing spondylitis (AS) is a chronic inflammatory arthritis that predominantly affects the axial skeleton in adolescent patients. The natural history of the disease remains poorly characterized. In this study, we combined GC-MS and LC-MS techniques to evaluate the major metabolic changes in the plasma of AS patients in view of metabonomics. Univariate and multivariate analysis were employed for altered metabolite comparison and pattern recognition. Application of supervised partial least-squares discrminant analysis to either GC-MS or LC-MS data allowed accurate discrimination of AS patients from normal controls, demonstrating its potential diagnostic utilization. In addition, AS patients presented elevated plasma concentrations of proline, glucose, phosphate, urea, glycerol, phenylalanine and homocysteine but reduced levels of phosphocholines, tryptophan and a bipeptide - phenylalanyl-phenylalanine. In the context of their involved metabolic pathways, the identified metabolites were discussed accordingly. This investigation primarily proved that integrated chromatography-mass spectrometry and integrated uni- and multi-variate statistical analysis facilitated metabonomics to be a more promising tool in disease research.