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Data sets in metabonomics or metabolic profiling experiments are becoming increasingly complex, which is hard to analyze without appropriate methods. The use of chemometric tools, such as orthogonal signal correction (OSC), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), (orthogonal partial least squares discriminant analysis (OPLS-DA) make the data dimension and interpretation much easier. Here a system method based on PCA, OSC-PLS-DA for metabonomic data analysis was showed; Furthermore, U-plot, as a visualized tool was used for the biomarkers discovery. As an example, dataset from RAC water extract administrated spleen deficiency rats plasma collected by LC/MS/MS was used to demonstrate this method. As a result, PCA was an useful tool for metabonomic dataset dimension reduction, OSC is an powerful data filter, U-plot based on OSC-PLS-DA was proved to be an effective, time saving tool for data interpretation and biomarkers discovery. In conclusion, the a system method shown by this paper is suitable for the matabonomic study.