Several authors [10; 11; 12; 13; 14; 15; 16; 17; 18] have studied vibroacoustic signals and rock properties using the Fourier transform to time waveform. [PERSON] et al. [11] found that the spectrum of each rock is distinct from those of other rocks, but within each rock type, the states of the spectra are similar. The dominant frequencies of each rock can be used to determine and predict the properties of the rock. The study above showed that it is possible to install acoustic sensors on drilling machines and to process the propagated acoustic signals to detect the type of rocks. In his work [13], [PERSON] illustrated the application of a vibration sensor and a special broadband acoustic sensor method to distinguish specific time-frequency characteristics generated by drilling in different rock types. His research found that vibration and acoustic spectral characteristics are different and can be used to recognise rock types. Using multiple regression analysis, [PERSON] et al. [19] tried to estimate the relationships between the noise level produced during rotary drilling and rock properties concerning drilling parameters. [PERSON] et al., in their work [20], proposed a new method for the automatic identification and recognition of microseismic and explosive rock fracture signals based on their time-frequency spectrum characteristics. [PERSON] et al. [21] proposed a model to identify rock hardness during tunnelling based on analysing various signals (vibration signals, acoustic emission signals, cutting current signals, and temperature signals). He based its design on multisensory information fusion and the Dempster-Shafer theory. Rock disintegration is one of the basic processes of mineral extraction. The drilling process is economically, energetically, and ecologically very demanding. Therefore, the optimal setting of mode parameters, the optimisation of energy consumption, the prediction of drilling tool wear, drilling efficiency, and the recognition of drilled rocks are all being investigated in this field [1; 2; 22; 23]. [PERSON] et al. [23] investigated the response characteristics of parameters when drilling into a rock mass. A comprehensive analysis of multiple sets of parameters found that the identification of the layered rock interface can be recognised. Their study confirmed that the material's structure affects some drilling parameters. Therefore, many authors study the relationships between the petrophysical and petrographic properties of rocks [24; 25; 26]. The authors [PERSON] and [PERSON] [27] discussed underground rocks' physical, geochemical, and geophysical properties under various combinations of stresses and pressure environments. Their publication provides examples of problems that occur during mining at greater depths, increases in the in situ stress and pore pressure, and temperature changes. These problems include borehole and tunnel instabilities, casing failures, water rushes into active mining areas, rock and coal spills, and mining-induced seismicity. Several researchers have used complex machine learning and deep learning models to determine classification models [28; 29; 30; 31]. In their study, [PERSON] et al. [32] found a clear response pattern between rock strength and sound pressure levels at constant rotations and thrust rates. This research aims to classify the rock type based on the vibration signal propagated during rotary drilling in an experimental laboratory. The motivation for this research is to consider the benefits of knowing, with a high reliability, which type of rock is disintegrated during deep drilling. The identification and determination of the type of rock according to the vibration signal will help to predict drilling progress and select appropriate operating parameters when disintegrating rock. This study consists of several parts. Section 2 describes the properties of disintegrated rocks, the methods of conducting experimental drilling, the methods of vibration signal acquisition, and the procedures for the research task.