This paper synthesizes the experiment carried out during two years with the MACAO knowledge acquisition method within the Sisyphus project. It relates our contribution to the project purpose of clarifying the definition and use of problem-solving methods when acquiring knowledge and designing a knowledge base. The lessons we derive from this work concern the definition, structure, building and use of the problem-solving model in MACAO, as well as the steps in the methodology.Firstly, we present the MACAO methodology, the associated software and its modelling structures. Next, we explain our interpretation of the expert's protocol given as an example of problem solving, and we report decisions taken about implicit knowledge. Then, we detail how the expertise is modelled with MACAO and we describe the model. This model is used to simulate the solving of the two problems given as possible inputs to the system to be designed. These cases provide support in explaining the acquisition process. They also prove the importance of an explicit problem-solving method in the model to guide the knowledge acquisition and the system design from the model. Comparing the problem solutions helps to evaluate how the model resists changes, and its behaviour when dealing with conflicting situations.These results highlight limitations in MACAO concerning the knowledge representation structures and the tool supporting the methodology. We finally discuss evolutions aiming at an easier identification and representation of the problem-solving method and at its use for knowledge elicitation and for the system design. We also consider possible extensions of the representation language in order to model the problem solving at different levels of abstraction.