A simulation is defined and tested for oceanic constituent estimation in case II waters, for the future medium resolution imaging spectrometer (MERIS) oceanic remote sensing instrument, using singular valued decomposition (SVD) and artificial neural networks (ANN) inversion techniques. The SVD technique, which bears a close resemblance to multivariate statistic techniques has previously been successfully applied to the problem of chlorophyll estimation from case I waters. In this study, a model is developed for the calculation of oceanic surface reflectance, as a function of the three major constituents which contribute to the optical properties of the water, (chlorophyll like pigments, yellow substance and sediments). The oceanic models have been validated using optical data acquired in the North Sea (1994) using the MARAS instrument. This surface reflectance is used to predict top of atmosphere radiance, which is then inputted to the MERIS instrument model. The algorithms are implemented on the simulated data to provide robust algorithms for the estimation of chlorophyll, sediment and yellow substance concentrations. The results of this investigation are presented with emphasis on recommendations for algorithm development, pre-processing and sampling strategies.