In this paper, a Field Theory based adaptive resonance neural network algorithm FTART, which combines the advantages of Adaptive Resonance Theory and Field Theory, is proposed. FTART employs a unique approach to solve the conflicts between instances and extend classification regions dynamically. So that it does not need user to manually configure hidden units, and achieves fast training speed and high predictive accuracy. Moreover, a method named Statistic based Producing and Testing, which has the ability of extracting comprehensive and accurate symbolic rules from trained FTART,is proposed.Experimental results show that the symbolic rules extracted via this method can commendably describe the function of FTART.