A new land cover classification methodology is proposed in this report. The idea is based on the following assumption; a land cover category is composed of several land cover elements and is identified by texture of these elements. Land cover elements can be extracted by clustering of the target image data. The texture can be measured by co-occurrence matrix for the extracted land cover elements. The three-layered feed forward neural network driven by the co-occurrence matrix is utilized as a classifier in the proposed method. In this study, the seven clustering methods and the number of land cover elements (16, 32, 64, 128, 256) were evaluated. As the result, the non-hierarchical disperse cluster split methods and 128 land cover elements showed the best classification accuracy. The proposed method showed the 3%, 14%, 22%, 24% and 39% higher classification accuracy than neural network classifiers driven by co-occurrence matrix for pixel value in local area, texture features (vector) extracted co-occurrence matrix for pixel value, pixel values (spectral vector) of a single pixel, pixel values of 3*3 pixels and a conventional maximum likelihood pixel wise classifier, respectively.