The aero engine gas path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, the principle of gas path electrostatic monitoring is briefly introduced. In view of the limited storage space and computation resource of the engine in-suit equipment, the fast symbolic time series analysis method is proposed to process the gas path electrostatic monitoring data for on-line fault detection. A case study is carried out on a data set acquired during a turbojet engine reliability testing program. It is fund that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data under different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults.