Abstract: In this paper, a novel cluster algorithm is proposed to make training sample data converge to cluster centers via an iterative procedure without consideration of predeterminated center number. The novel cluster algorithm provides cluster centers and variances for Gaussian membership function to establish 1 order TSK fuzzy neural network. After establishment, a hybrid algorithm is implemented to tune network parameters, namely, to adjust premise parameters with gradient descent algorithm and consequent parameters with recurrent least squares respectively. Finally, simulation results are given to demonstrate the effectiveness of the implementation of this novel cluster algorithm in fuzzy neural networks.