Our paper presents an analysis of the effect of data selection uncertainty for the teaching and testing sets in the black box model (multilayer perceptron type of artificial neural networks) using the bootstrap method on the accuracy of forecast and control of activated sludge sedimentation (SE) and the sludge volume index (SVI). The calculations show that sludge sedimentation, and hence also the sludge volume index, can be predicted based on the wastewater quality indicators and biological reactor operating parameters. The presented analyses also confirmed the significant influence of the neural network model structure on the uncertainty of estimating biological reactor operating parameters (mixed liquor suspended solids, concentration of oxygen) which, in practical considerations, leads to problems of continuous control of the sludge sedimentation capacity.