In recent years, 3D volumetric attributes have gained wide acceptance by seismic interpreters. The early introduction of the single-trace complex trace attribute was quickly followed by seismic sequence attribute mapping workflows. Three-dimensional geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to very simple, easy-to-understand measures of a waveform or surface morphology. However, not all geologic features can be so easily quantified. For this reason, simple statistical measures of the seismic waveform such as rms amplitude and texture analysis techniques prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we coupled structure-oriented texture analysis based on the gray-level co-occurrence matrix with self-organizing maps clustering technology and applied it to classify seismic textures. By this way, we expect that our workflow should be more sensitive to lateral changes, rather than vertical changes, in reflectivity. We applied the methodology to a remote sensing image and to a 3D seismic survey acquired over Osage County, Oklahoma, USA. Our results indicate that our method can be used to delineate meandering channels as well as to characterize chert reservoirs.