Due to induction motor's properties of resilience, durability and cost eectivenessthey are the most widely used electrical transducers across all industries. Naturally,because of these properties of ruggedness they are often selected for use in harshconditions increasing their risk of mechanical wear and tear. Almost 80 percent of alldrives across industries are induction motors [1], due to which they end up consumingalmost 40 to 50 percent of a country's total generating capacity (power) [2]. Henceit is vital that the induction motors run to their maximum eciency to avoid lossof revenue and power. Over time, a number of methods and techniques have beendeveloped to monitor health of the motor and for fault diagnosis.Usage of such methods however has led to increased cost to the industry as mostof the techniques employed need to have equipment on every motor individually togain valuable and necessary data. Moreover they would also require skilled man-power to be able to correctly use these methods. Hence to mitigate the cost andman-hours required for diagnostics of induction motors, a possible way to performsuch diagnostics on multiple motors simultaneously was inquired by EPRI.This thesis answers the question by providing a way to perform electrical diagnosticson multiple motors simultaneously by being able to distinguish between two motorsignals measured from a common power source(the source is split to feed the motors inparallel). The process outlined in the thesis comprises of estimating individual rotorfrequency of the motors by using synchronous detection and phong's grid estimationalgorithm for maximizing stator current magnitude and for estimating the phase ofsignal of the estimated frequency.