Concentrations of chlorophyll and suspended sediment in surface water are tow important parameters for monitoring inland water quality. In the open ocean, it is not difficult to derive empirical algorithms relating received radiances at remote sensors to concentrations of water quality parameters. However, in optically complex inland water the task is difficult due to overwhelming of spectral signature of chlorophyll by other organic components present in high concentration. Neural Networks have been successful in modeling various geophysical transfer function. In this study, NN is used to model the transfer function between chlorophyll and sediment concentrations, and above-water upwelling reflectance simulated at three Landsat Thematic Mapper visible bands form spectrometer data. The developed model could estimate chlorophyll better than conventional regression analysis. In estimating surface chlorophyll, Root Mean Square Error (RMSE) for neural network was found to be < 15 percent, while the same for regression was > 30 percent. In estimating suspended sediment, regression performed comparatively better than in chlorophyll estimation with an RMSE of 22 percent. The corresponding RMSE for neural network was 12 percent. Upon validation, the trained model is used to get spatial distribution of the two water quality parameters from the Landsat Thematic Mapper imagery. Prior to this, the LandsatTM digital number values are converted to equivalent spectrometer-derived reflectances with a regression between these two quantities at sampling locations, thus taking into account the atmospheric effects which are often difficult to be satisfactorily quantified in inland waters.