A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and alpha-pinene (P) mixture. The bioreactors were tested with two types of shock loads, i.e., long-term (66 h) low to medium concentration loads, and short-term (12 h) low to high concentration loads. M and HS were removed in the BTF, reaching maximum elimination capacities (ECmax) of 684 and 33 gm(-3) h(-1), respectively. P was removed better in the second stage BF with an ECmax of 130 gm(-3)h(-1). The performance was modelled using two multi-layer perceptrons (MLPs) that employed the error backpropagation with momentum algorithm, in order to predict the removal efficiencies (RE, %) of methanol (REM), hydrogen sulphide (REHs) and alpha-pinene (REp), respectively. It was observed that, a MLP with the topology 3-4-2 was able to predict REM and REHS in the BTF, while a topology of 3-3-1 was able to approximate REp in the BF. The results show that artificial neural network (ANN) based models can effectively be used to model the transient-state performance of bioprocesses treating gas-phase pollutants. (C) 2016 Elsevier B.V. All rights reserved.