The prediction of chemosensitivity is a challenging problem in the management of cancer. In the present study, a metabonomic approach was proposed to assess the feasibility of chemosensitivity prediction in a human xenograft model of gastric cancer. BALB/c-nu/nu mice were transplanted with MKN-45 cell line to establish the xenograft model. The mice were then randomized into treatment group (cisplatin and 5-fluorouracil) and control group (0.9% sodium chloride), and their plasma were collected before treatment. Metabolic profiles of all plasma samples were acquired by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer (HPLC/Q-TOF-MS). Based on the data of metabolic profiles and k-Nearest Neighbor algorithm, a prediction model for chemosensitivity was developed and an average accuracy of 90.4% was achieved. In addition, a series of endogenous metabolites, including 1-acyl-lysophosphatidycholines, polyunsaturated fatty acids and their derivatives, were determined as potential indicators of chemosensitivity. In conclusion, our results suggest that the proposed metabonomic approach allows effective chemosensitivity prediction in human xenograft model of gastric cancer. The approach presents a new concept in the chemosensitivtiy prediction of cancer and is expected to be developed as a powerful tool in the personalized cancer therapy.