In this paper, two versions of Adaptive Artificial Neural Network (AANN) model based Generic Model Control (GMC) [AANNGMC1 & AANNGMC2] are proposed for nonlinear processes. The proposed controllers consist of online parameter estimation of a purely data driven ANN model based on past measurements using Extended Kalman Filter, and control computation to track the predicted output derivative along its desired reference trajectory in a GMC frame-work. A industrial multi-product semi-batch polymerization reactor case study posed as a challenge problem for temperature control has been considered to apply the proposed AANNGMC1 and AANNGMC2 strategies at supervisory level, as cascade control configurations along with Proportional Integral (PI) controller i.e. AANNGMC1-PI and AANNGMC2-PI. The proposed configurations have shown better controller performance in terms of temperature tracking as well as smoother input profiles compared to the exact model based GMC-P1 and standard PI-PI control schemes for two different products, and among the two configurations, AANNGMC2-PI has exhibited better performance. The proposed schemes are found to be versatile although they are based on a purely data-driven models with online parameter estimation. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.