A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results. (c) 2007 Elsevier Ltd. All rights reserved.