Rotating machinery holds a noteworthy role in industrial applications and covers a wide range of mechanical equipment. Vibration analysis using signal processing techniques is generally utilized for condition monitoring of rotary machinery and engineering structures in order to prevent failure, reduce maintenance cost and to enhance the reliability of the system. Empirical mode decomposition (EMD) is amongst the most substantial non-linear and non-stationary signal processing techniques, and it has been widely utilized for fault detection in rotary machinery. This paper presents the EMD, time waveform and power spectrum density (PSD) analysis for localized spur gear fault detection. Initially, the test model was developed for vibration analysis of single tooth breakage of spur gear at different RPMs and then specific fault was introduced in driven gear under different damage conditions. The recorded data, by wireless tri-axial accelerometer, was then analyzed using EMD and PSD techniques and results have been plotted. Results depicted that EMD algorithms are found to be more functional than the ordinarily used PSD and time waveform techniques.