The retrieval of Ocean Color (OC) data products is here investigated by using Multi Layer Perceptron (MLP) neural nets. Synthetic data have been generated for this scope with a forward OC model. These samples have then been used to train and assess the MLP performance considering different seawater types (WTs) with optical properties driven by: chlorophyll (Chl-a), colored dissolved organic matter (CDOM), and non-pigmented particulate matter (NPPM), as well as a mixture of Chl-a, CDOM and NPPM (denoted MIXI). Acknowledging that MLP classification results represent WT posterior probabilities, an integrated machine learning approach is set up by joining MLPs for data regression and classification in a composite scheme. Results indicate that this approach is valuable to support the use of regional ocean color inversion schemes by decomposing the overall challenge in sub-components, optimally addressing each of them, and combining the individual solutions in a principled framework.