Statistical classification methods consisting of the k-nearest neighbor algorithm (k-NN), a probabilistic clustering procedure (PCP), and a novel method that incorporates outcrop-based thickness criteria through the use of well log indicator flags are evaluated for their ability to distinguish fluvial architectural elements of the upper Mesaverde Group of the Piceance and Uinta Basins as distinct electrofacies classes. Data used in training and testing of the classification methods come from paired cores and well logs consisting of 1626 wireline log curve samples each associated with a known architectural element classification as determined from detailed sedimentologic analysis of cores (N = 9). Thickness criteria are derived from outcrop-based architectural element measurements of the upper Mesaverde Group. Through an approach that integrates select classifier results with thickness criteria, an overall accuracy (number of correctly predicted samples/ total testing samples) of 83.6% was achieved for a four-class fluvial architectural element realization. Architectural elements were predicted with user's accuracies (accuracy of an individual class) of 0.891, 0.376, 0.735, and 0.985 for the floodplain, crevasse splay, single-story channel body, and multi-story channel body classes, respectively. Without the additional refinement by incorporation of thickness criteria, the k-NN and PCP classifiers produced similar results. In both the k-NN and PCP techniques, the combination of gamma ray and bulk density wireline log curves proved to be the most useful assemblage tested.