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The sensorless drive system is more versatile due to its small size and low cost. Therefore it is advantageous to use the sensorless system where the speed is estimated by means of a control algorithm instead of measuring. This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives using neural networks. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. In the estimator design using motor parameters and monitored stator voltages and currents also taking the motor speed as a variable. Performance analysis of speed estimator with the change in motor parameters especially resistances of stator and rotor is presented. Its performance under fault condition is also examined. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator especially under fault condition.