Index Based Reasoning (IBR) is proposed for chatter detection and tool wear estimation by using the torque signal data of rotary dynamometers during the end milling operation. The IBR is a simple reasoner which classifies the incoming signals with the help of lookup table after the most descriptive features are identified with pre-processing. Easy implementation, selection of programming or self-learning modes depending on the characteristics of the application, and capability of distributing the smart nodes to physically separated locations make the IBR a good candidate for diagnostic of manufacturing processes either at the tool, machine, and work cell level. For chatter detection, programming mode was used for preparation of the lookup table. For wear estimation, lookup table was automatically generated from very limited training cases (only three) by detecting the trend. The IBR performed well on the experimentally collected data.