The objective of this paper is to perform an uncertainty analysis of the predictions of the Benchmark Simulation Model (BSM) No 1, when comparing four activated sludge control strategies. The Monte Carlo procedure – an engineering standard, was used to evaluate the uncertainty in the predictions of the BSM1. As input uncertainty of the BSM1, the biokinetic parameters and influent fractions of ASM1 were considered, while for the model predictions the Effluent Quality (EQ) and Operational Cost (OCI) indexes were focused on. The resulting Monte Carlo simulations were presented using descriptive statistics indicating the degree of uncertainty in the predicted EQ and OCI. The Standard Regression Coefficient (SRC) method was used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQ predictions the most. The results show that control strategies including an ammonium (SNH) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (SNO) uncertainty, but increasing the economical costs and their variability as a trade-off. Finally, the maximum specific autotrophic growth rate (μA) was found responsible for causing the majority of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. ηg (anoxic growth rate correction factor) and ηh (anoxic hydrolysis rate correction factor), becomes less important when a SNO controller manipulating an external carbon source is implemented. These results are meaningful (and expected in a way) from a control engineering point of view: Properly tuned feedback controllers will make the process more robust towards input disturbances, attempting to maintain the process at a predefined setpoint despite input uncertainty (input disturbances), thus ensuring that the output uncertainty of the process is lower compared to for example an open-loop plant. Overall it is found useful to perform uncertainty and sensitivity analysis when comparing different control strategies based on model predictions.