The suitability of mathematical models used to extract kinetic information from correlated data constitutes a significant issue in fluorescence correlation spectroscopy (FCS). Standard FCS equations are derived from a simple Gaussian approximation of the optical detection volume, but some investigations have suggested this traditional practice can lead to inaccurate and misleading conclusions under many experimental circumstances, particularly those encountered in one-photon confocal measurements. Furthermore, analytical models cannot be derived for all measurement scenarios. We describe a novel numerical approach to FCS that circumvents conventional analytical models, enabling meaningful analyses even under extraordinarily unusual measurement conditions. Numerical fluorescence correlation spectroscopy (NFCS) involves quantitatively matching experimental correlation curves with synthetic curves generated via diffusion simulation or direct calculation based on an experimentally determined 3D map of the detection volume. Model parameters are adjusted iteratively to minimize the residual differences between synthetic and experimental correlation curves. In order to reduce analysis time, we distribute calculations across a network of processors. As an example of this new approach, we demonstrate that synthetic autocorrelation curves correspond well with experimental data and that NFCS diffusion measurements of Rhodamine B remain constant, regardless of the distortion present in a confocal detection volume.