Superabrasive grind wheels are used for the machining of brittle materials such as tungsten carbide. Stochastic modeling of the wheel topography can allow for statistical bounding of the grind force characteristics allowing improved surface quality without sacrificing productivity. This study utilizes a machine vision method to measure the wheel topography of diamond microgrinding wheels. The results showed that there are large variances in wheel specifications from the manufacturer and that microgrinding wheels suffer from statistical scaling effects that increase wheel-to-wheel variability in the topography. Analysis of the static grit density values measured on the microgrinding wheels showed that the distributions provided by both analytic stochastic and numerical simulation models accurately predicted the static grit density within a significance level of 5%. Utilizing only manufacturer-supplied specifications caused the models to predict the static grit density with errors as large as 25.3% of the predicted value leading to a need for improved wheel tolerancing and in situ wheel measurement. The spacings between the grits on the wheel surface were shown to be independent of direction and can best be described by a loglogistic distribution.