This paper describes an algorithm for above-cloud aerosol (ACA) retrievals from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm, based on neural networks (NNs), has been trained on synthetic measurements and has been applied to the processing of one-year PARASOL data. The algorithm makes use of three subsequent NNs: (1) for the detection of liquid clouds, (2) for the retrieval of aerosol properties for ACA cases, and (3) an NN forward model to evaluate the goodness-of-fit of the retrieval. The NN’s theoretical capability of retrieval is investigated by several synthetic data studies. It is shown that the NNs retrieve ACAOT₅₅₀ (above cloud aerosol optical thickness, at 550 nm), AE_440–670 (Ångström exponent, between 440 and 670 nm), and SSA₅₅₀ (single scattering albedo, at 550 nm) with an RMSE (root mean squared error) of ∼ 0.1 on ACAOT₅₅₀, ∼ 0.4 on AE_440–670 and ∼ 0.04 on SSA₅₅₀ in synthetic experiments. Finally, comparison between the NN retrievals and adjacent PARASOL-RemoTAP clear-sky retrieval in 2008 shows good agreement within the range expected from the synthetic study.