A neural network trained using the counterpropagation algorithm to produce stochastic conditional simulations is applied and evaluated on a real dataset. This type of network is a non-parametric clustering algorithm not constrained by assumptions (i.e. normal distributions) and is well suited for risk and uncertainty analysis given spatially auto- correlated data. Detailed geophysical measurements from a slab of Berea sandstone are used to allow comparison with a traditional geostatistical method of producing conditional simulations known as sequential Gaussian simulation. Equiprobable simulations and estimated fields of air permeability are generated using an anisotropic spatial structure extracted from a subset of observation data. Results from the counterpropagation network are statistically similar to the geostatistical methods and original reference fields. The combination of simplicity and computational speed make the method ideally suited for environmental subsurface characterization and other earth science applications with spatially auto- correlated variables.