Gearboxes are the fundamental elements of rotational systems to provide speed adjustment ratios from a rotating power source to another. In industrial applications, the existence of any kind of faults in rotational systems may be hazardous unless the early detection and maintenance procedures are applied. Incipient types of faults such as a few chipped or worn teeth at the gearbox mechanism may deteriorate and cause the maladjustment of the rotation and even the mechanism may stop to rotate which may cause loss of the production. Preventive maintenance strategies such as monitoring of the vibration signals and comparison of the frequency domain irregularities with normal operation case with healthy gearbox elements is essential to ensure safe and accurate rotational speed transmission in industrial systems. In this work, frequency domain characteristics of three different pinion conditions; healthy, a chipped tooth, and three consequent worn teeth are analyzed, and frequency domain features are proposed for classification of the pinion state. Proposed features obtained from the statistical properties of the coefficients of third level Wavelet packet decomposition. After feature extraction process, classification of the gear condition is made with different Support Vector Machine based classifiers and significant classification success observed with the proposed technique.