The capabilities of Support Vector Machine (SVM) applied to aero-engine fault diagnosis (i.e.classification of multiple faults) were investigated.The gas path components of a jet engine were selected to gather datasets for the evaluation of the classification capabilities.With the proposed approach,24 sets of component single fault testing data from the datasets were classified into 5 single faults and 16 sets of multiple faults testing data were classified into 8 faults.The single faults are low pressure compressor (LP) fault, high pressure compressor (HP) fault,low pressure turbine (LT) fault,high pressure turbine (HT) fault and no fault;while the multiple faults are LP+HP,HP+HT,HT+LT,LP+LT and LP+HP+LT+HT faults.There is no misclassification for all of the testing data using SVM.When the datasets are masked with noise as great as 12% in single faults and 10% in multiple faults,the effectiveness and robustness of the fault diagnosis algorithms are still satisfactory.