A data analytic technique is described for use in automated cervical cytology. The method entails the application of a two-dimensional Fourier transform to histogram data obtained by flow microfluorometry and the subsequent use of the Fourier coefficients as parameters for pattern classification. Analyses were performed on 186 samples including material from 62 positive and 124 negative cases. Cell suspensions were stained with propidium iodide and fluorescein isothiocyanate, and red and green cytofluoresence were measured simultaneously. The two-dimensional histogram data were normalized, the Fourier transform was applied, and a multivariate classifier based on 30 coefficients was assembled using a training set of half the original series. Performance was then assessed on the remaining cases. Overall accuracy was 79.6%, with a false-positive rate of 14.8% and a false-negative rate of 31.2%. The potential applicability of this approach as the basis of a practical screening system is discussed.