A neural network-based methodology of quantifying the association between the design variables and the users’ performances

User performance is highly correlated with design variables of a system. Such association can be described as display–control relationship. In this study, a neural network-based methodology is proposed to identify and quantify the association among design variables (inputs) and to compute their relative influences (RIs) on the two performance measures (outputs) of user response time and response accuracy, using artificial neural network, generalised regression neural network, support vector regression (SVR), multiple linear regression and response surface model. Based on the results of the comparison, it is found that neural network-based methods are more reliable than SVR-based methods in computing the RI of design variables. As a result of our analysis, the best option for optimising each of the measures is suggested. Some useful observations about the design of man–machine systems are also presented, discussed and visualised. In the study of man–machine systems, quantitative methods are seldom adopted for examining the mappings between various displays and controls under a variety of operating conditions. The major contribution of this study is to provide some insights into the usefulness of quantitative methods in evaluating man–machine design in terms of display–control compatibility and to extract explanatory information from renowned black box systems such as neural networks.