A neural network-based method is developed to assess the vertical distribution of (1) chlorophyll a concentration ([Chl]) and (2) phytoplankton community size indices (i.e., microphytoplankton, nanophytoplankton, and picophytoplankton) from in situ vertical profiles of chlorophyll fluorescence. This method (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm) uses as input only the shape of the fluorescence profile associated with its acquisition date and geo-location. The neural network is trained and validated using a large database including 896 concomitant in situ vertical profiles of High-Performance Liquid Chromatography (HPLC) pigments and fluorescence. These profiles were collected during 22 oceanographic cruises representative of the global ocean in terms of trophic and oceanographic conditions, making our method applicable to most oceanic waters. FLAVOR is validated with respect to the retrieval of both [Chl] and phytoplankton size indices using an independent in situ data set and appears to be relatively robust spatially and temporally. To illustrate the potential of the method, we applied it to in situ measurements of the BATS (Bermuda Atlantic Time Series Study) site and produce monthly climatologies of [Chl] and associated phytoplankton size indices. The resulting climatologies appear very promising compared to climatologies based on available in situ HPLC data. With the increasing availability of spatially and temporally well-resolved data sets of chlorophyll fluorescence, one possible global-scale application of FLAVOR could be to develop 3-D and even 4-D climatologies of [Chl] and associated composition of phytoplankton communities. The Matlab and R codes of the proposed algorithm are provided as supporting information.