Active automata learning allows to learn software in the form of an automaton representing its behavior. The algorithm SL$$^{*}$$, as implemented in RALib, is one of few algorithms today that allows learning automata with data parameters. In this paper we investigate the suitability of SL$$^{*}$$ to learn software in an industrial environment.For this purpose we learned a number of industrial systems, with and without data. Our conclusion is that SL$$^{*}$$ appears to be very suitable for learning systems of limited size with data parameters in an industrial environment. However, as it stands, SL$$^{*}$$ is not scalable enough to deal with more complex systems. Moreover, having more data theories available will increase practical usability.