It is desirable for automated object recognition using computer vision systems to emulate the human capacity for recognition of shapes invariant to various transformations. We present an algorithm, based on a Fuzzy Associative Database approach, which uses appropriately invariant metrics and a neuro- fuzzy inference method to accurately classify both two- and three-dimensional objects (using different metrics for each). The system is trained using a small number of images of each object class under varying degrees of the transformations, and as we show experimentally, is then able to identify objects under other non-explicitly-trained degrees of the transformations.