Operator training is a significant challenge for corporations, such as electrical generating plants, that rely on complex systems to perform specific tasks reliably and accurately. The complexity of the systems that operators must master means extensive memorization of interconnections and interrelations between system components is required. Systems like this have traditionally been learned through static diagrams and flowsheets. The use of simulator training is also employed because of its exceptional experiential learning; however, the high costs associated with simulator setup and maintenance limit their widespread use, which is exacerbated by the need to retrain operators to maintain regulatory compliance. Operators must instead train using complex, static diagrams to gain an understanding of the systems which they are learning to control. To facilitate and accelerate learning, it is therefore advantageous on many levels to transform the existing corpus into interactive, dynamic diagrams to clarify and reinforce the information contained in the static diagrams without increasing the trainees’ reliance on simulators. This proposed research project will produce a system for parsing technical diagrams and, using computational models, integrate the systems they represent into an augmented reality (AR) environment. The system will capture an image of the diagram and produce a virtual model of the structure described by the diagram, including the symbols, the labels, and the connections between them. This structural model will be connected with existing dynamical models to provide a trainee with an interactive and flexible learning environment.