A link between amino acid composition and optimal pH in G/11 xylanase was established. A back propagation neural network (BPNN) was used as the mathematical tool and a uniform design method was employed to optimise the architecture of the BPNN. Results showed that the calculated and predicted pHs fitted the optimal pHs of xylanase very well, with mean absolute percentage errors (MAPEs) of 3.02 and 4.06%, mean square errors (MSEs) of 0.19 and 0.19 pH unit and mean absolute errors (MAEs) of 0.11 and 0.19 pH unit respectively. The new model performed better in fitting and prediction compared with a previously reported model based on stepwise regression. (c) 2006 Society of Chemical Industry.