Pattern recognition has provoked a great interest in the last decades due to the use of the computers. As a consequence, numerous engineering applications have been developed. The complexity of a pattern recognition system is high because much of the information available in the real life is presented in the form of complex patterns, suffering from linear transformations even nonlinear deformations. The objective of this paper is to develop a model for invariant pattern recognition by combining Zernike moments and Fuzzy ART. The former works as invariant feature extractor while the letter acts as a robust classifier. The considered model will efficiently recognize patterns without taking in consideration the possible variations of position, rotation and scale. The experimental results shows the model can achieve high classification accuracy.