Least squares regression is highly unreliable when a strong collinearity structure is present among the predictors. Among several proposals introduced in the literature, principal component regression is a straightforward method to overcome the problem, even if it introduces a slight bias in the parameter estimation. This paper presents a simulation study to evaluate the use of principal component regression in the context of quantile regression and, focusing on the variability of the estimates and the model’s prediction ability.