The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.