Abstract. Recent years have seen a growing interest in the characterization of the pore morphologies of reservoir rocks and how the spatial organization of pore traits affects the macro behavior of rock–fluid systems. With the availability of 3-D high-resolution imaging, such as x-ray micro-computed tomography (µ-CT), the detailed quantification of particle shapes has been facilitated by progress in computer science. Here, we show how the shapes of irregular rock particles (pores) can be classified and quantified based on binary 3-D images. The methodology requires the measurement of basic 3-D particle descriptors (length, width, and thickness) and a shape classification that involves the similarity of artificial objects, which is based on main pore network detachments and 3-D sample sizes. Two main pore components were identified from the analyzed volumes: pore networks and residual pore ganglia. A watershed algorithm was applied to preserve the pore morphology after separating the main pore networks, which is essential for the pore shape characterization. The results were validated for three sandstones (S₁, S₂, and S₃) from distinct reservoirs, and most of the pore shapes were found to be plate- and cube-like, ranging from 39.49 to 50.94 % and from 58.80 to 45.18 % when the Feret caliper descriptor was investigated in a 1000³ voxel volume. Furthermore, this study generalizes a practical way to correlate specific particle shapes, such as rods, blades, cuboids, plates, and cubes to characterize asymmetric particles of any material type with 3-D image analysis.