Specialists and non-specialists in artificial neural networks (ANN) must closely interact in many applications, including structural sensing. The non-specialists must be aware of ANN-specific terminology, capabilities, and connecting concepts for effective collaboration. An instructional approach for ANNs is described that progresses from practical concepts to guided MatLab-based experimentation. Back propagation-trained multilayer perceptron neural networks are presented with an emphasis on parallel processing and training characteristics. The one-week instructional module has a lecture to convey terminology and structure, detailed examples to illustrate the training process, and guided application-based exercises. The MatLab neural-networks toolbox provides a transparent learning environment in which the students focus on network design and training concepts rather than the tool itself. Learning effectiveness was evaluated in an applications-oriented sensors curriculum. Instructional resources including realistic problems are webaccessible. These resources may be adjusted for different degrees of challenge and for simpler or more realistic problem solving.