Mechanical rock cutting became very common. The introduction of different sensors to monitor the performance of the machine, and immediately automatically control the cutting process is attractive and profitable. Acoustic sensors have been used in metal cutting, and an attempt is being made to use them in rock cutting . Many researchers have tried to use acoustic emission (AE) to track the process of rock cutting and monitor changes in it. Many of them used a rock-mounted acoustic sensor, some of them relied on the analysis of the AE wave signal, and others looked at the frequency spectrum (FFT), and analyzed the effect of change in cutting conditions on the location of the major dominant frequencies (MDFs) . Some of these researches lacked to consider the effect of the distance between AE source and the acoustic sensor location, and also the necessity of improving the AE signal quality so that it gives correct numbers, as well as the importance of analyzing the MDFs values and not the MDFs locations alone. A study was conducted to monitor the AE signals resulting from cutting concrete blocks of different strengths. cuts were made with different cutting conditions to study the effect on the AE signal. The frequency spectrum of the AE of rock cuttings was analyzed. The result of this research is that it was possible to differentiate between the different rocks during cutting, as well as to realize the changes in the cutting conditions, by analyzing the average peak of the AE waveform, but with a small error. This error was avoided by analyzing the average values of the MDFs in the frequency spectrum. The conclusion was that the sound of rock cutting is unique, and like all sounds, it has its unique frequency spectrum and frequency range, which if considered only without the rest of the frequencies, then improvement of accuracy will be achieved. It was recommended to design a system of AE differentiation of rock cutting based on the unique frequency spectrum of rock cutting sound.