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In automatic manufacturing systems, the quality of machining is greatly affected by the cutting tool condition. For example, excessive cutting tool wear could give rise to distortion, sometimes damaging machine parts; hence, incurring additional costs and complications in the production line. If the wear of the cutting tool can be predicted prior to damage, then machining can be altered to compensate for the damage resulting in better quality products. To accomplish this, an intelligent system applying efficient techniques is needed to predict cutting tool problems during machining. This paper proposes a methodology using artificial intelligence techniques. This methodology combines the selection and optimization abilities of genetic algorithm and the prediction characteristics of the neural network. The drive behind this work is to find an optimal trade-off in the system where the least needed sensory data is correlated to the cutting tool wear, without compromising on the accuracy. The objective of the improved system is to have a fast response time at a relatively cheap cost, while providing a warning in advance of potentially developing faults. The key advantage of this work is its ability to achieve accurate results and to cope with vast amount of highly unstructured data besides its robustness to noisy and sparse data.