In marble industry, it is of vital importance to determine the damaged discs on time to prevent possible industrial injuries. Therefore, in this study, it is proposed to classify the status of the cutting discs that are used while cutting the natural stones. To classify the deflections of the discs, 673 different experiments are performed. Cutting discs corresponding to four different damage classes (undamaged disc, less damaged disc, much damaged disc, and broken disc) are employed in the tests. Eight different parameters (cutting forces (F_x, F_y, F_z), noise, peripheral speed of the disc, current, voltage, power consumption) are measured and recorded in the experiments. For each experiment, mean values of different measured data are studied. Artificial neural networks are employed as classifiers. In the first stage, all of these mean values corresponding to eight parameters are selected as the input vectors of the artificial neural networks, whereas in the second stage, the dimension of input vector is decreased by leaving out the parameters one by one. In this stage, it is aimed to determine the most important parameter that caries much more information about the cutting process.