Recent advancements of 3D computer graphics hardware systems have made possible the handling of 3D volumetric data, and the amount of the available data has increased for various scientific fields. This paper proposes a pattern feature extraction method for 3D volumetric data. Pattern features are important for systems which require segmentation and classification. In this paper, the Laws texture energy approach is extended so that both 2D image data and 3D volumetric data can be handled. The Laws texture energy approach is a powerful technique for describing pattern features of 2D texture images, and it has been applied to various software applications. Our extension of the Laws texture energy approach enables direct extractions of pattern features from 3D volumetric data. Although the three dimensional extension increases the number of pattern features, the pattern features are reduced by combining similar pattern features. Our simulation software program attempts to reduce the number of shape features. The program rotates each feature along the x, y and z axes. The rotated features are evaluated if they are identical to other features, and the identical features are combined together to reduce the number of pattern features. For the experiments, artificially synthesized 3D solid textures are analyzed by using the 3D extended Laws features, and solid textures are classified by linear discriminant analysis (LDA). Our preliminary experiments show that the three dimensional extension of the Laws texture energy approach successfully classifies a certain database of 3D volumetric data.