An unsupervised fuzzy neural network classification algorithm has been developed and applied to perform feature abstraction and classify a large number of training instances into a small number of clusters. A fuzzy neural network learning model has been developed by integrating the unsupervised fuzzy neural network classification algorithm with a genetic algorithm and an adaptive conjugate gradient neural network learning algorithm. The learning model has been applied to the domain of image recognition. The performance of the model has been evaluated by applying it to a large-scale training example with 2304 training instances. An average computational speedup of eight is achieved by the new algorithm.