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Monitoring rotating machinery is often accomplished with the aid of vibration sensors. The vibration sensor signals contain a wealth of complex information that characterizes the dynamic behavior of the machinery. Transforming this information into useful knowledge about the health of the machine can be challenging due to the presence of extraneous noise sources and variations in the vibration signal itself. This is particularly true in situations in which the rotating machinery is monitored under varying loads and/or speeds. In order for any gained knowledge or insight into the health of machinery to be useful, it must be actionable. This is achieved by detecting incipient faults as early as possible. A novel approach to vibration monitoring that employs a multivariate similarity-based modeling (SBM) technique to characterize the expected behavior of time synchronous averaged spectral features is shown to enable the detection in rotating machinery. This in turn facilitates the assessment of machine health and enables fault diagnostics and ultimately prognostics. SBM has been applied successfully to a variety of non-vibration related multi-sensor, health monitoring applications. Our new approach builds off of these experiences and a combination of signal processing algorithms to expand the overall applicability of SBM into single sensor vibration monitoring. We discuss an approach to gearbox fault monitoring using vibration data and SBM. This new approach is described in detail and is applied to actual H-60 gearbox vibration data acquired from seeded fault tests conducted by U.S. Naval Air Systems Command (NAVAIR) at the Helicopter Transmission Test Facility (HTTF) in Patuxent River, MD in 2001 and 2002.