Condition monitoring techniques were applied to a reciprocating compressor in order to determine if faults were present in a system. Through the use of vibration based sensors, fault monitoring of the crank-side discharge valve springs was accomplished. Data was collected through a range of injected fault conditions and analyzed through the use of discrete wavelet transformations. The wavelet coefficients produced were transformed into a six-dimensional feature space though the use of first and second order statistics. By using a support vector machine classifier, the nominal and faulted condition data was used to train a fault monitoring classifier. This classifier was verified through the use of additional test data, and resulted in classification rates of 90% and above. This result is based on the trial of a multitude of different wavelets and support vector kernels in order to achieve the optimal performance for the dataset.