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.