Subspace System Identification is by now an established methodology for experimental modelling. The basic theory is well understood and it is more or less a standard tool in industry. The two main research problems in subspace system identification that have been studied in the recent years are closed loop system identification and performance analysis.The aim of this contribution is quite different. We have asked an industrial expert working in process control a set of questions on how subspace system identification is used in design of model predictive control systems for process industry. As maybe expected, it turns out that a main issue is experiment/input design. Here, the difference between theory and practice is rather large mainly due to implementation constraints, but also lack of knowledge transfer. Motivated by the response from the expert, we will discuss several important user choices problems, such as optimal input design, merging of data sets and merging of models.

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