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Although activated sludge process is a very widely used biologicalprocess in wastewater treatment plants (WWTP), and there areproperly functioning control loops such as that of dissolved oxygen,in practice, this type of plant requires a major time investment onthe part of the operator, involving many manual operations.Treatment plants work well most of the time, as long as there are not unforeseen occurrences. Normal operatingsituations (generally similar to design conditions) can be treatedmathematically by using efficient control algorithms. However, there aresituations in which the control system cannot properlymanage the plant, and in which the process can only be efficiently managedthanks to the operator‘s experience. This is a case in which aknowledge-based system may be useful. One of the difficulties inherent tothe development of a knowledge-based system is to obtain the knowledge base(i.e., {\it knowledge\ acquisition}), specially whendealing with a wide, complicated and {\it ill-structured} field.Among the aims of this work arethose to show how semi-automatic knowledge acquisition tools could helphuman experts to organize their knowledge about their domain and also, tocompare the power of different approaches of {\it knowledge\ acquisition} to the same database.In this paper are presented the results obtained fromapplying two different classification techniques to the development of knowledge-bases for the management of an activated sludge process.