We simulate the interpretation process by the testing of preformed working hypotheses. A clinical syndrome, "bronchial obstruction," is described by a set of suitable parameters (FEV1, MMEF, Raw, etc.). For a given patient, this set forms a normalized vector. It has to be compared with equivalent data derived from patients which fulfilled the criteria for the clinical syndrome in question. If the patient's vector has a similar direction as the vector of the collective, the working hypothesis is accepted. The length of the vector is then used to quantify the severity of the functional disturbances in verbal terms ("slight," "moderate," "severe"). The limits used for severity grading and the typical parameter pattern for the given syndrome are adapted to the user's criteria by a built-in learning capability. On the other hand, the assembled data may be used for the training of newcomers. The use of vector algorithms allows for a high flexibility of our program with respect to all methods used in lung function testing.