During the past decade a significant research has been carried out in a large number of universities around the world in machinery fault diagnosis. The new development has tremendous potential to systematize and provide a sound scientific basis for machine condition monitoring and fault diagnosis. It will make it possible to automate the condition monitoring process, develop smart devices and perform accurate diagnostics remotely in concert with the current trends of IoT. This workshop will provide attendees with the applications of recent development in signal processing techniques for diagnosing machine defects using vibration and motor current signals. The emphasis will be on cepstrum analysis, spectral kurtosis and order tracking although other techniques such as MED, DRS, and phase/amplitude demodulations will also be described. I will explain the fundamental principles in a simplified manner using physical basis rather than the underlying complex mathematical structure. Hands-on demonstration using a machinery fault simulator will be provided. We will collect the data with seeded faults and analyze the results. I will also use case histories to illustrate and explain the application of these techniques. Attendee will also learn how to utilize their existing vibration analysis tools to extract more information from the vibration signal using the currently available data collectors and analysis software. The benefits will include how to rationally determine the sampling rate, resolution, max frequency, and how to distinguish if you are missing a frequency component in your selected resolution.