In the present paper, we report an algorithm method to retrieve the oceanic constituent concentrations (CHL, SPM, and CDOM) in Case II waters. The method is derived from the radiative transfer simulations and is subsequently applied in the Artificial Neural Network (ANN) techniques. Information on absorption and total scattering of pure sea water, as well as absorption of phytoplankton and associated particles are taken from measurements or parameterisations in published literatures, and information on absorption of coloured dissolved organic matter and nonagal particles, as well as scattering of marine particles were derived from the COASTLOOC data set. Additionally, a new model on the backwards scattering probability model is used, of which probability is a function of the organic particulate matter and the total particulate matter (SPM) ratio and wavelength. Such defined inherent optical properties are input as a radiative transfer code in order to generate a synthetic data set of hemispherical reflectance spectra, subsequently used for the training of various ANNs to find the best approximation of the functional relationship between ocean colour and oceanic constituent concentrations. The performance of the ANN-based retrieval schemes is assessed by applying it to the hemispherical reflectance spectra contained in the COASTLOOC data set and PMNS data set, and comparing the retrieved oceanic constituent concentrations to those actual measurements. The results show that the ANN-based algorithms have good performance in retrieval of oceanic constituents for ocean colour measurements in Case II waters.