Precision machining technique is widely growing in the new competitive environment and the need for precision components is ever growing. dimensional variations in the component are a key factor affecting precision. this project reveals a new technology, an auxiliary system capable of adapting to variations in a machining process and controls it in real time. this projects studies in details about the development of the portable intelligence system (pis) which uses neural network to predict dimensional variations in the component during machining operation and aims to control it. the control is achieved by the use of a servomotor controlled portable modular fixture. the system is integrated to a cnc milling machine and uses process data to train the neural network and make corrections on the lateral feed of the work piece during the machining process to reduce dimensional variations in the component. two sets of experiments are conducted one without the pis installed and other with the pis installed. the results are compared and control charts are drawn to measure the dimensional variations from the specified mean thickness of the component. the results show that there is significant improvement in precision of milling operation and the process capability had improved to near six sigma standards.