The design and implementation of a neural network-based system combining the outputs of several sensors (acoustic emission, force and spindle motor current) for monitoring progressive tool wear in a single point turning operation is described. Multichannel autoregressive series model parameters and power spectrum amplitudes are used as inputs to the network. The objective of the system is to extend the range of machining conditions over which the system performs successfully. A basic architecture for multiple sensor systems is outlined. Results of recent research to implement a real-time monitoring system are presented.