This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.