The article presents the concepts of control systems that use methods of automatic analysis and interpretation of signals. These issues are presented through the description of three control strategies: simple closed-loop control, control using a process of classification, and using a signal understanding technique. The systems based on the discussed concepts are illustrated with examples of controlling a milling machine and an autonomous vehicle. In addition, in more detail, a task of learning human motor activities is described. This task, due to the nature of the controlled object, which is a human being, is extremely difficult. The article shows that the advanced control process, in which the control algorithm is selected and its parameters are adapted to the current situation, may be implemented through the use of the classification process and machine learning methods in general. Changing the algorithm is also possible using signal understanding techniques. These techniques, utilizing models of the objects, allow to predict the long-term effects of the control process. The ability to build control systems that operate in the above manner is of huge practical importance. The aim of this article is to describe the methods of automatic signal interpretation used in control processes and identify the main problems related to their use. The key problems refer to the acquisition of expert knowledge by the system. In order for this knowledge to be effectively transferred, the methods used in the system should have a high level of explainability. Showing the essential nature of this feature is the main outcome of this work.