Petrophysical rock classification is an important component of the interpretation of core data and well logs acquired in complex reservoirs. Tight-gas sandstones exhibit large variability in all petrophysical properties due to complex pore topology resulting from diagenesis. Conventional methods that rely dominantly on hydraulic radius to classify and rank reservoir rocks are prone to rock misclassification at the low-porosity and low-permeability end of the spectrum. We introduce a bimodal Gaussian density function to quantify complex pore systems in terms of pore volume, major pore-throat radii, and pore-throat radius uniformity. We define petrophysical dissimilarity (referred to as orthogonality) between two different pore systems by invoking the classic “bundle of capillary tubes” model and subsequently classify rocks by clustering an orthogonality matrix constructed with all available mercury injection capillary pressure data. The new method combines several rock textural attributes including porosity, pore-throat radius, and tortuosity for ranking reservoir rock quality in terms of flow capacity. We verify the new rock classification method with field data acquired in the Cotton Valley tight-gas sandstone reservoir located in the East Texas basin. The field case shows that the new method consistently identifies and ranks rock classes in various petrophysical data domains, including porosity-permeability trends, pore-size distribution, mercury injection capillary pressure, and NMR transverse relaxation time () spectra. Relative permeability curves, which are difficult to measure in the laboratory for tight rocks, are quantified with Corey-Burdine’s model using the bimodal Gaussian pore-size distribution and are validated with core data.