Classification of earth terrain from satellite radar imagery represents an important and continually developing application of microwave remote sensing. The basic objective of this paper is to derive more information, through combining, than is present in any individual element of input data. Multispectral data has been used to provide complementary information so as to utilize a single SAR data for the purpose of land-cover classification. More recently neural networks have been applied to a number of image classification problems and have shown considerable success in exceeding the performance of conventional algorithms. In this work, a comparison study has been carried out between a conventional Maximum Likelihood (ML) classifier and a neural network (back-error-propagation) classifier in terms of classification accuracy. The results reveal that the combination of SAR and MSS data of the same scene produced better classification accuracy than either alone and the neural network classification has an edge over the conventional classification scheme.