A new operational non-satellite-specific algorithm for the simultaneous retrieval from satellite data of phytoplankton chlorophyll content (chl), suspended minerals (sm), and dissolved organics (doc) in both clear and turbid waters is presented. It contains an array of neural networks providing input for the Levenberg -Marquardt multivariate optimization procedure as the final retrieval tool. With a given accuracy threshold, the developed algorithm is sufficiently robust for data with noise up to 15% for certain hydro-optical conditions. To avoid inadequate retrieval results, the algorithm identifies and eventually discards the pixels with inadequate atmospheric correction and/or water optical properties incompatible with the applied hydro-optical model. The validity of the developed algorithm was tested for Lake Michigan. Two dedicated field campaigns in the vicinity of the Kalamazoo River mouth have been conducted concurrently or quasi-concurrently with SeaWiFS and MODIS overpasses. In addition, some archival shipborne measurements of mostly chl and occasionally sm and doc were employed to validate the facility of the algorithm. Notwithstanding the aforementioned shipborne data limitations, the conducted comparison of the ground truth and retrieved data on the water quality parameters in Lake Michigan is strongly indicative of the algorithm's operational efficiency.