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Monitoring Land use/Land cover (LULC) changes and develop a model is utmost important for the developing cities to avoid its haphazard growth, especially in fringe areas. With the help of satellite imageries available for last few years or decades, with different spatial and spectral resolution, this task has become easier and reliable. But, to give the model solution for any real world problem there is a need to have reliable input data in appropriate format. Hence to provide solution for uncontrolled land use changing pattern, it is mandatory to perform image analysis on raw images. This study aims to review different image classification methods and its utility for Land use/Land cover analysis. The paper includes demonstration of supervised image classification with both maximum likelihood and minimum distance method.