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Automatic trace shape recognition via neural network analysis was utilized on a 70-km 2 3-D seismic data volume from the giant Matzen field, Vienna Basin, Austria, to identify depositional facies in slope and basin-floor settings. Seismic wave shape at key wells was used for establishing links between seismic response and facies. Continuity analysis, proportional horizon slicing, voxel-body analysis, and highfrequency stratigraphic analysis involving an additional 50+ wells were integrated to the neural network results. This methodology can rapidly isolate and highlight reservoirquality facies and clarify facies interrelationships. When carried out in a high-frequency stratigraphic framework, facies style and resulting internal heterogeneity are more accurately predicted. Utilizing the wave-shape analysis with different windows above the zone of interest made it possible to discriminate between basin-floor fan sandstone, shelf-edge delta/slope fan sandstone, and slope and basin mudstone in the falling-stage systems tract of the overlying depositional cycle.