Char morphology is an important characteristic when attempting to understand coal behavior and coal burnout. In this study, an augmented algorithm has been proposed to identify char types using image analysis. On the basis of a series of image processing steps, a char image is singled out from the whole image, which then allows the important major features of the char particle to be measured, including size, porosity, and wall thickness. The techniques for automated char image analysis have been tested against char images taken from ICCP Char Atlas as well as actual char particles derived from pyrolyzed char samples. Thirty different chars were prepared in a drop tube furnace operating at 1300 degrees C, 1% oxygen, and 100 ms from 15 different world coals sieved into two size fractions (53-75 and 106-125 mu m). The results from this automated technique are comparable with those from manual analysis, and the additional detail from the automated system has potential use in applications such as combustion modeling systems. Obtaining highly detailed char information with automated methods has traditionally been hampered by the difficulty of automatic recognition of individual char particles.