A basic image of objects is extracted from a two-dimensional image of objects. Geometrical elements of the objects are extracted from the extracted basic image. The objects to be recognized are identified by searching a combination of the geometrical elements which match a geometrical model and then utilizing candidate position/orientation of the objects to be recognized, said candidate position/orientation being determined from a relationship in relative position between the combination of geometrical elements and the geometrical model. Mesh cells fixed to the geometrical model are mapped on the basic image based on the candidate position/orientation. In addition, verification is made as to whether an image of the geometrical model mapped by the candidate position/orientation is accurately matched with an image of one of the objects to be recognized, through a neural network to which values got from the basic image included in the individual mesh cells are to be applied as input values. Combination weight factors employed in the neural network are learned according to the verified results. It is also possible to recognize the multi-purpose objects according to how to learn the combination weight factors.