Micro-electrical discharge machining is a stochastic process where the interaction between the materials and the process parameters are difficult to understand. Monitoring of the process becomes necessary to achieve the dimensional accuracy of the micro-featured components. Although thermo-mechanical erosion is the most accepted material-removal mechanism, it fails to explain the material removal with very short pulse duration. Alternative postulate like electrostatic force-induced stress yielding provides a stronger argument, rising ambiguity over the material-removal process in the micro-electrical discharge machining regime. In this work, it was found that the stress waves released from the material during micro-electrical discharge-machining process indicate material removal by mechanical deformation and fracture mechanism. These stress waves were captured using the acoustic emission sensor. The discharge pulses were captured by voltage measurement and classified using voltage gradient and machining time duration into three major categories, open pulse, normal pulse and arc pulse. The acoustic emission signal features were extracted and identified by time–frequency–energy distribution analysis. A feed-forward back-propagation neural network mapping of the pulse instances was performed with the obtained acoustic emission signature. The time–frequency–energy distribution analysis of the acoustic emission and the scanning electron microscope images of the craters provide conclusive evidence that the material is removed by mechanical stress and fracture. The feed-forward back-propagation network model was trained to predict the discharge categories of the pulse instances with AE signal inputs which can be used for monitoring the material-removal mechanism in micro-electrical discharge machining operation.

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