A hierarchical Bayes (HIER) model for the quantitative genetic analysis of performance data was compared to a model based on the use of Henderson's average numerator relationship matrix (ANRM) when some pedigrees are characterized by uncertain paternity. A simulation study consisted of ten datasets characterized by 30% of animals having uncertain paternity for each of two traits: one having moderate heritabilities for direct and maternal genetic effects on weaning weight (WWT) and another having a high heritability for direct genetic effects on postweaning gain (PWG). Posterior inference on the variance components was very similar between the two models. In an application to WWT and PWG data from Brazilian Herefords, posterior inference on variance components was also very similar between ANRM and HIER. Furthermore, rank correlations on posterior means for genetic effects between the two models exceeded 0.90. Nevertheless, large differences in posterior means between these two models were observed for some animals. Furthermore, animals with uncertain paternity had generally larger posterior standard deviations of genetic effects using the HIER model compared to the ANRM model, likely because the HIER model infers upon sire assignment probabilities. Bayesian model choice criteria consistently favored the HIER model over the ANRM model in both simulation and Hereford data analysis studies