This paper presents multiple functional neural fuzzy networks (FNFN) fusion using fuzzy integral (FI). Since the classifiers are able to complement each other, the fusion of multiple classifiers overcomes the limitations of applying a single classifier. In addition, the FI is a better decision-combination scheme than the majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple FNFN classifiers with FI is proposed to achieve data classification with higher accuracy than existing traditional methods. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Computer simulations for the Iris, Wisconsin breast cancer, and wine classifications show that the fusion of multiple FNFNs using FI can perform better than existing traditional methods.