The need to improve quality and decrease scrap rate while increasing the production rate is motivating industry to consider untended machining as viable alternative. On-line monitoring of a machining process is the key component to success for an untended machining operation. In this chapter, a framework for sensorbased intelligent decision-making systems to perform on- line monitoring is proposed. Such a monitoring system interprets the detected signals from the sensors, extracts the relevant information, and decides on the appropriate control action. Emphasis is laid on applying neural networks to perform information processing, and to recognize the process abnormalities in a machining operation. A prototype monitoring system is implemented to demonstrate the working mechanism. For successful implementation of the developed intelligent monitor, an instrumented force transducer is designed for signal detection and is used in a real time turning operation. A neural network monitor based on feedforward back-propagation algorithm is developed and tested under the machining of advanced ceramic materials and steel. The monitor is trained by the detected cutting force signal, the measured surface finish, and the observed tool wear. The superior learning and noise suppression abilities of the developed monitor enable high success rates for monitoring in machining process.