Artificial Neural Networks have gained increasing popularity as an alternative to statistical methods for classification of remote sensed images. The superiority of neural networks is that, if they are trained with representative training samples they show improvement over statistical methods in terms of overall accuracies. However, if the distribution functions of the information classes are known, statistical classification algorithms work very well. To retain the advantages of both the classifiers, decision fusion is used to integrate the decisions of individual classifiers. In this paper a new unsupervised neural network has been proposed for the classification of multispectral images. Classification is initially achieved using Maximum Likelihood and Minimum-Distance-to-Means classifier followed by neural network classifier and the decisions of these classifiers are fused in the decision fusion center implemented using Majority-Voting technique. The results show that the scheme is effective in terms of increased classification accuracies (98%) compared to the conventional methods.