Remote sensing sensors operating in the optical region of the spectrum capture reflected &/ or emitted electromagnetic radiation from the object/ features which facilitates their identification/ detection . Beginning with a few spectral bands in early 1970s for example in Landsat Multispectral sensor now the user community has access to remote sensing images with hundreds of spectral bands viz. Hyperion image with 232 spectral bands. The challenges faced by image analyst is how to minimize the data analysis time without sacrificing the information content of remote sensing images. The information present in remote sensing imagery depends to a large extent on various factors like spatial radiometric resolutions and amount of noise present in the imagery. It points to minimizing the number of spectral bands by using appropriate image processing techniques into a few spectral vectors/ indices. Towards this end several spectral indices/ spectral transformation approaches namely image entropy Principal Component analysis Optimum Index Factors etc. have been developed and used for inventory and monitoring of water resources extent distribution and temporal behaviour of water bodies. The focus of the article is on selection of spectral features using the image processing tools available in ERDAS/ IMAGINE which are indicative of the information content analysis of Landsat-8 operational Land imager for water resource management. This work studies the use of the principal component analysis as a preprocessing technique for the classification of Multi spectral images