Micro tools have been widely used in industry primarily by biomedical and electronic equipment manufacturers. The life of these cutting tools is extremely unpredictable and much shorter than conventional tools. Also, these miniature tools, with a diameter of less than one millimeter, cannot be inspected by an operator without the aid of a magnifying glass. In this paper, evaluation of the intensity variation of reflected laser beam light from the cutting tool surface is proposed to estimate tool conditions. Two encoding methods are proposed to obtain a small and meaningful set of data from the intensity variation readings of one tool rotation. The encoded data are classified by using a simple threshold method and Adaptive Resonance Theory (ART2)-type neural networks. The proposed encoding and classification approaches are tested with over one hundred sets of data.