Abstract— This paper proposes the use of cepstrum editing technique as a signal pre-processing algorithm to enhance spall related vibration features in rolling element bearings for the purpose of size quantification and fault prognosis. Cepstrum editing technique has been proposed and utilized in a number of contexts which include: signal separation; bearing diagnosis enhancement through pre-whitening and operation modal analysis through extracting the forcing function. In this paper, the cepstrum editing technique is utilized to enhance the weak step response event originating from the entry of the rolling element into the spall region. The cepstrum editing technique is used to remove the transfer path effect (high quefrency liftering). The effectiveness of the cepstrum editing technique is demonstrated on two sizes of a naturally originated and propagated inner race spalls from a high-speed test rig. The use of the proposed technique helps in clarifying the entry event at a number of locations when visually examining the time domain signal. The further processing of the signal using bearing synchronous averaging for instance will give a definite measure for the size of the spall. In addition, features extracted with the aid of the cepstrum editing can be applied effectively in neural networks (NNW) and artificial intelligence (AI) algorithms to quantify spall