Of the many parameters used in characterizing the performance of a CD-SEM, resolution is one of the more difficult to define and measure. Most of the evaluation techniques heretofore applied are prone to operator bias, making comparisons between different machines, and even long-term monitoring of the same machine, difficult. The technique presented here is based on the common method of obtaining a “contrast” versus spatial frequency curve, or modulation transfer function, by calculating the two-dimensional Fourier transform of an SEM image. The resolution is defined, similar to the Sparrow method in light optics, as the point at which the signal contrast goes to zero, coincident with the highest passable spatial frequency, a somewhat less arbitrary definition than that of Rayleigh. The crux of the method is in the techniques used to extract the data from the noise to obtain this zero crossing point. This paper both describes the significance, and presents the procedure, for a “one-click” method of evaluating resolution. The algorithm relies on methods of numerical, rather than image, analysis, with averaging and smoothing techniques used to circumvent the often large inherent signal-to-noise ratio, thereby obtaining more consistent results than previous methods; such as measuring spaces between objects, calculating amplitude ranges (e.g., 10-90, 20-80, or 25-75), or matching contours to Fourier transform cutoffs by eye. The method has also been found to be less sensitive to image brightness and visual contrast differences than these and some more sophisticated methods. The processing methodology; including flat top-Gaussian windowing, frequency specific averaging, and extrapolated smoothing techniques; used to minimize or eliminate transform artifacts, signal processing bandwidth limitations, and signal-to-noise issues; respectively, are described.