This paper presents a fault diagnosis algorithm of control surfaces of small fixed-wing aircraft to reduce maintenance cost or to improve repair efficiency by estimation of fault occurrence or part replacement periods. The proposed fault diagnosis algorithm consists of ANPSD (Averaged Normalized Power Spectral Density), PCA (Principle Component Analysis), and GC (Geometric Classifier). ANPSD is used for frequency-domain vibration testing. PCA has advantage to extract compressed information from ANPSD. GC has good properties to minimize errors of the fault detection and isolation. The algorithm was verified by the accelerometer measurements of the scaled normal and faulty ailerons and the test results show that the algorithm is suitable for the detection and isolation of the control surface faults. This paper also proposes solutions for some kind of implementation problems.