Hybrid systems are dynamical systems of both continuous and discrete nature and constitute an important field of control systems theory and engineering. On the other hand, intelligent data processing has become one of the most critical devices of modern computer based systems as these systems operate in environments featuring increasing uncertainty and unpredictability. While these two approaches set completely different objectives, modern cyber-physical systems, taken as variants of hybrid systems, seem to constitute a field of increasing interest for applying intelligent techniques. Moreover, the examples of, not so recent, intelligent control systems are suggestive for considering a study on getting intelligent techniques close to hybrid systems. In this paper we present the experimental investigation we undertook in this direction. More specifically, we present and discuss the experiments carried out using Acumen a hybrid systems modeling and simulation environment. Without urging towards setting and solving questions of conceptual order we tried to figure out whether it is possible to represent intelligent behavior using a tool for modeling dynamical systems focusing on the study of its ability to permit the representation of both continuous and discrete intelligent techniques, namely, Reinforcement Learning and Hopfield neural networks. The results obtained are indicative of the problems related to the specific computational context and are useful in deriving conclusions concerning the functionality that needs to be provided by such modeling and simulation environments, in order to allow for the coexistence of hybrid systems and intelligent techniques.