This study proposes a novel approach to monitoring the grinding wheel during the dressing operation by using acoustic emission (AE) signals and the statistical Counts method. AE signals were acquired during the dressing passes and processed in MATLAB^®. The Counts matrices were segmented according to the grinding wheel rotation, and the metric termed z-ratio, which combines mean and standard deviation statistics, was calculated for each subwindow. The vectors were then filtered, normalized, and represented in polar coordinates. The results demonstrate the method’s ability to track the evolution of dressing and detect grinding wheel eccentricity, offering a promising tool for the indirect monitoring of the surface conditions of the grinding tool during the dressing operation of conventional grinding wheels.