The ability of acoustic emission signal in detecting failure in steel structure make it a reliable method in modern non-destructive testing. Generally, the detection of failure relies on the features of signals originated from the source of acoustic emission signal. The aim of this study is to distinguish the acoustic emission signal, based on selected features from two different frictional sources namely metal rubbing and plastic rubbing events on a plate of mild steel panel. During data acquisition, the acoustic emission signals were recorded using a piezoelectric based sensor placed at three specific locations on the mild steel panel. The recorded signals are found to be highly dependent upon the acoustic properties of the source of events. Compared to plastic rubbing, metal rubbing event is found to produce higher absolute energy (<16,333,461aJ) and amplitude (up to 95dB) for all three sensors. Furthermore, the cumulative absolute energy value registered different patterns for both events. The features produced by AE waveforms from each frictional source can be used to characterize and classify the signals. The established correlation between acoustic emission features suggests that this technique can be a valuable tool in predicting various features of signals produced by such metal materials. © 2020 IEEE.