Recent advances in technology have brought major breakthroughs in deep learning techniques. In this work, we elaborate on such techniques for output data of image processing performed on craquelure patterns in historical paintings. Historical painted objects, especially panel paintings, with their long environmental history, exhibit complex crack patterns called craquelures. These are cracks in paintings that can be referred to as ‘edge fractures’ as they are initiated from the free surface. The analysis has been conducted on the set of selected craquelure patterns on which recent deep learning methods i.e. Neural Networks algorithm is implemented and the results of such self-learning process are discussed.