The demand for miniaturized components with feature sizes as small as tens of microns and tolerances as small as 0.1 microns is on the rise in the fields of aerospace, electronics, optics and biomedical engineering. Micromilling has proven to be a process capable of generating the required accuracy for these features and is an alternative to various non-mechanical micro-manufacturing processes which are limited in terms of cost and productivity, especially at the micro-meso scale. The micromilling process is on the surface, a miniaturized version of conventional milling, hence inheriting its benefits. However, the reduction in scale by a few magnitudes makes the process peculiar and unique; and the macro-scale theories have failed to successfully explain the micromilling process and its machining parameters. One such characteristic is the unpredictable nature of tool wear and breakage. There is a large cost benefit that can be realized by improving tool life. Workpiece rejection can also be reduced by successfully monitoring the condition of the tool to avoid issues. Many researchers have developed Tool Condition Monitoring and Tool Wear Modeling systems to address the issue of tool wear, and to obtain new knowledge. In this research, a tool wear modeling effort is undertaken with a new approach. A new tool wear signature is used for real-time data collection and modeling of tool wear. A theoretical correlation between the number of metal chips produced during machining and the condition of the tool is introduced. Experimentally, it is found that the number of chips produced drops with respect to the feedrate of the cutting process i.e. when the uncut chip thickness is below the theoretical minimum chip thickness.