The correct control and prediction of Wastewater Treatment Plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. In this respect, it is known that qualitative information ---coming from microscopic examinations and subjective remarks--- has a deep influence on the activated sludge process. In particular, it influences the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input-output model of this variable is thus a central concern in order to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity, and the very high amount of missing information make the use of traditional techniques difficult, or even impossible. Despite these problems, and through the combined use of several soft computing methods ---rough set theory and artificial neural networks, mainly--- reasonable prediction models are found. These models also serve to show the different importance of variables and give insight to the process dynamics.