In this study, a three-dimensional quasi-random nano-structural model based on the Monte Carlo method is proposed to evaluate effective catalyst utilization in fuel cell catalyst layers. Reflecting the intrinsically inhomogeneous nano-morphology of fuel cell catalyst layers, statistical analyses are performed to statistically compare various types of fuel cell electrodes to gain fundamental insight into the effects of morphological nano-structures on the catalyst utilization. For the detailed morphological analysis, a series of multi-component distributions at a 95% confidence level is randomly generated to deduce the statistical variations of the effective transport paths of ternary catalyst components. The statistical nano-morphology model is validated against published experimental data with good agreement. Subsequently, the morphological configuration of the vertically aligned carbon nanotube (VACNT) catalyst layers is simulated to determine the principal nano-design factors for improved catalyst utilization in the same stochastic manner which is used for carbon-supported catalyst layers. In the VACNT catalyst layers, all of the Pt/CNTs are successfully interconnected and therefore, all solid conducting carbon nanotubes can be utilized as electric current paths. Numerical results reveal that despite the relatively poor interconnections of the ion and mass transport paths, the statistical average catalyst utilization of VACNT is significantly improved when compared to conventional catalyst layers. It is also found that the ionic current paths of the VACNT catalyst layers can be considered as a catalyst utilization determining factor and an adequate amount of ionomers are necessary to promote successful ionic conduction. Finally, the average catalyst utilizations for both the regularly patterned in-line and staggered VACNT catalyst layers are compared with results for randomly distributed VACNTs. (C) 2016 Elsevier Ltd. All rights reserved.