As EBSD techniques improve, researchers are rapidly gaining access to quantities of high-caliber information previously unavailable. However, these benefits bring their own drawbacks. Engineers must either learn to cope with large amounts of data, or they must be more selective about which data is captured. In either case, machine learning techniques may play an important role. Data mining techniques can be used to extract knowledge from large databases, while other machine learning methods enable the identification of critical features, and the efficient search for such features at the data acquisition phase. One particular application of these techniques involves the investigation of fracture and fatigue mechanisms. Methods are required for finding and recording critical event inception. The development of in-situ test equipment, and high-resolution microscopy techniques (such as high-resolution EBSD: HREBSD) have placed invaluable tools into the hands of researchers. Nevertheless, practical considerations limit the volume of material that can be carefully monitored during a given testing regime. Machine learning techniques offer a promising framework for enhancing efficiency in the search for critical events. This paper presents initial efforts to develop an intelligent microscopy environment for EBSD users based upon machine learning methods. The test bed for the study will include ductility studies in magnesium, exploiting recent advances by the authors in the area of HREBSD.