the database of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. the knowledge discovery in databases (kdd) appears as a possible solution to find out such relations aiming at converting information into knowledge. however, not all data presented in the bases are useful to a kdd. usually, data are processed before being presented to a kdd aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. this work consists in the use of rough sets theory, in order to pre-processing data to be presented to self-organizing map neural network (hybrid architecture) for clusters analysis. experiments' results evidence the better performance using the hybrid architecture than self-organizing map. the paper also presents all phases of the kdd process.