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Current efforts to apply machine learning techniques to the automated recognition of microstructures of welded joints of structural steels will be presented. The machine learning approach is based on the Convolution Neural Network (CNN) and deep learning techniques, that have attracted a lot of attention for automated image identi铿� cation and regression. This work is in progress, however promising results were obtained for the basic microstructures during the 铿� rst tests. A critical implementation of the commonly agreed de铿� nitions used in classi铿� cation of microstructures (Thewlis 2004, IIW Doc. IX-1533-88) is being used to train the algorithm. The characteristic microstructures such as Primary Ferrite, Pearlite, Ferrite and Second Phases, Acicular Ferrite and Martensite are being used a main category for the classi铿� cation. Surrounding and localized features of the microstructures can be analyzed by using different training techniques. In this context, the choice of traditional point counting vs. 鈥渋mage segmentation鈥� (de铿� ning microstructural regions in a fashion similar to painting a map) will also be discussed.The approach proposed, aims to open the possibility of automated quality control of microstructures and of a 鈥渂ig data鈥� approach to the study of microstructures. Ultimately, the classi铿� cation and quanti铿� cation
of numerous micrographs can be used to anticipate the mechanical properties from a micrograph.