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This study presents a Web-based remote monitoring system for evaluating degradation of machine tools using an ART2 neural network. A number of studies on the monitoring of machine tools using neural networks have been reported. However, when normal condition is changed due to any factors such as maintenance, tool change and etc. or new failure signal is generated, these algorithms need to be entirely retrained in order to accommodate such new signals. To cope with such problems, this study proposes a new remote monitoring system using ART2 in which new signals when required are simply added to the previously trained classes. The proposed remote monitoring system can monitor degradation as well as failure of machine tools. To show the effectiveness of the proposed approach, it is experimentally applied to monitoring of a simulator similar to the main spindle of a machine tool, and the results show that the proposed system can be extended to monitoring of real industrial machine tools and equipment.