The IUCN Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate "true" population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with "true" population declines. Five major insights were revealed: 1) under all measurement error levels tested and low process error classifications were reasonably accurate, 2) scalar and matrix models give roughly the same rate of misclassifications but the distribution of errors differs, 3) matrix models lead to greater over-estimation of extinction risk than under-estimations, 4) process error tends to contribute to misclassifications to a greater extent than measurement error, and 5) more misclassifications occur for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa, i.e. taxa with low growth rates, under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. This article is protected by copyright. All rights reserved.