The DNA histogram obtained by flow cytometry can be considered as the product of an "ideal" measurement column vector and a measurement distortion matrix. In order to extract the ideal histogram from the real data, the measurement distortion matrix is commonly presumed to be a family of Gaussian coefficients that are centered on the diagonal. We have designed a feedback-controlled curve-fitting procedure that reconstructs the ideal histogram from the real data through successive iterations. The optimum coefficient of variation (cv) for the family of Gaussians in the measurement distortion matrix is determined from an analysis of the sums of squares of fits of the computed DNA histogram to the real data over an appropriate range of trial cv. Since this method assigns a Gaussian to each and every data channel, it permits the resolution of closely spaced multiple aneuploid G1 peaks in clinical samples. The effects of high frequency noise that may be present in the data can be attenuated by multiplying the real data histogram by a Gaussian matrix with cv close to but smaller than that of the measurement distortion matrix.