Supercritical extraction of lanolin from raw wool with modified CO2 (5% ethanol) at temperatures above the melting point of lanolin (T = 36-42 ºC) is difficult to model because of the multicomponent diffusion in the liquid layer. In this work, a neural network model is proposed based on the experiments previously published by our research group. Experimentally, the extraction of a 100-cm 3 packed bed of raw wool depends on five variables, i.e., temperature (60-80 ºC), pressure (120-200 bar), solvent mass flow rate (3-5 kg/h), wool packing density (127-318 kg/m 3 ), and time (~ 1 h). A nonlinear autoregressive exogenous (5,3,1) neural network was designed and trained with the experimental data augmented using an empirical Weibull statistical function. This correctly predicts the lanolin breakthrough at the extractor exit with only ± 0.42% error. The simple arithmetics of neural network allows a fast optimization with Genetic Algorithm to find optimum operation conditions for the extraction process. Keywords High pressure extraction • Lanolin • Neural networks • Genetic algorithm • Weibull List of symbols a Mass transfer area, 1/m A Const. defined by Eq. (12), 1/s AARD% Absolute average of relative deviations, Eq. (22) b Weibull parameter Bi Biot number for mass B Const Eq. (16), kg bi NN bias neuron c Const Eq. (17) c' Const Eq. (14) Cg Fluid-phase concentration, kg/m3 C L Liquid-phase concentration, kg/m3 C L0 Initial lanolin liquid, kg/m3 D Lanolin diffusivity, m2/s K Henry equilibrium const. k G Fluid-side mass transfer coef, m/s γ (m, x) Incomplete gamma function, Eq. (19) Me, Mo Median and mode, h