The grinding process has been mainly used fur finishing metal products as final machining stage. But chatter vibration and bum of a workpiece have a bad effect on the machined surface and should be detected in modern grinding process. This paper deals with a fault detection of the cylindrical plunge grinding process by power parameters. During the grinding process the power signals of an induced motor were sampled and used to determine the relationship between fault and change of power parameters. A neural network was used far detecting the grinding fault and an influence of power parameters to the grinding fault was analyzed.