The annual usage of heat for the demand of heating systems in municipal sector has been estimatedas about 650PJ. It is mostly addressed for the demand of central heating systems and hot waterconsumption. The mode of adopted solutions concerning regulation and control, as well as energymanagement system, essentially influence its consumption. In the case of residential buildings,the costs of energy constitute the greatest share related to the total cost of building maintenance. Providing buildings with modern digital systems for control and regulation of heating installationsis a basic condition enabling their rational usage. In currently employed solutions, algorithms PI or PID are usually applied. However, due to the non-linear properties of heating control systems, they do not secure proper quality. The sequences are often unstable and major control deviationsoccur. The application of neural networks is an alternative solution to those presently employed. They are especially recommended for adaptive control of non-stationary systems. Such cases occurin heating objects since they demonstrate non-linear properties with a great range of variability ofparameters; this especially refers to district heating equipped with flux-through heat exchangers.In this paper, a compile model of heating system control aided by neural networks is presented. The results of the investigation clearly prove the usefulness of such solutions, cause the qualityof control is much better than that one applied in traditional systems. Presently, works on theimplementation of the proposed solutions are under way.