The high-impedance arc fault (HIAF) is a result of the interaction between the current-carrying conductor and thehigh-impedance surface. This study presents a detailed and comparative time-frequency based analysis for the classification ofdifferent arcs, generated by the interaction of a broken conductor and different arcing surfaces. The real-time arcing voltagesignals are considered as the basis of the whole time-frequency based analysis from a medium voltage distribution line. In thisproposed approach, empirical mode decomposition (EMD) is used to study the real-time arc voltage signals of various surfaces.The intrinsic mode functions obtained by the application of the EMD technique is used as the input to various machine learningtechniques, which later successfully classifies different HIAFs from their harmonic footprints. The Stockwell transform (ST) isadditionally applied to the same test cases, and the classification of the arc is performed using similar learning algorithms to geta quantitative view of both outcomes of EMD and ST towards the selection of appropriate signal processing technique andmachine learning algorithm for the same. © 2020 The Institution of Engineering and Technology.