Seismic facies estimation is a critical component in understanding the stratigraphy and lithology of hydrocarbon reservoirs. With the adoption of 3D technology and increasing survey size, manual techniques of facies classification have become increasingly time consuming. Besides, the numbers of seismic attributes have increased dramatically, providing increasingly accurate measurements of reflector morphology. However, these seismic attributes add multiple “dimensions” to the data greatly expanding the amount of data to be analyzed. Principal component analysis and self-organizing maps (SOMs) are popular techniques to reduce such dimensionality by projecting the data onto a lower order space in which clusters can be more readily identified and interpreted. After dimensional reduction, popular classification algorithms such as neural net, K-means, and Kohonen SOMs are routinely done for general well log prediction or analysis and seismic facies modeling. Although these clustering methods have been successful in many hydrocarbon exploration projects, they have some inherent limitations. We explored one of the recent techniques known as generative topographic mapping (GTM), which takes care of the shortcomings of Kohonen SOMs and helps in data classification. We applied GTM to perform multiattribute seismic facies classification of a carbonate conglomerate oil field in the Veracruz Basin of southern Mexico. The presence of conglomerate carbonates makes the reservoir units laterally and vertically highly heterogeneous, which are observed at well logs, core slabs, and thin section scales. We applied unsupervised GTM classification to determine the “natural” clusters in the data set. Finally, we introduced supervision into GTM and calculated the probability of occurrence of seismic facies seen at the wells over the reservoir units. In this manner, we were able to assign a level of confidence (or risk) to encountering facies that corresponded to good and poor production.