The process performance of catalysts is crucially dependent on pore structure. The technique of low melting point alloy (LMPA) impregnation provides at any given pressure a visual measure of the penetration of accessible porosity. Polished sections of impregnated particles then reveal images of the spatial state of penetration of the alloy into accessible pores, which is effectively a visualisation of porosimetry. Theoretical interpretation can be undertaken using 3-D stochastic pore networks comprised of basic cylindrical pore segments, but randomised in up to five ways (diameter, length, tortuousness, roughness and cross-section). These 3-D random pore networks provide for both mercury porosimetry evaluation and theoretical image reconstruction of alloy penetrated sections. Experimental images of alloy penetration and theoretical images can both be readily scanned and quantified by image analysis (pixel accounting) procedures. An application to a large pore Ni/Alumina steam reforming catalyst (Dycat) is described. Close consistency between experiment and theory indicates that 3-D stochastic pore networks can provide a good quantification of particle pore structure.