The results of experimental investigation of acoustic emission (AE) source characterization in 2-D frames is reported in this paper. Determination of the AE source location and magnitude from the displacement field is an inverse problem. It is known that humans have remarkable sensory abilities to undo a large number of demanding inverse problems with little effort. Here, nature is modeled by implementing intelligent signal processing algorithms to solve the problems of the AE source location and magnitude determination. The signal processing algorithms implemented are; FFNN trained by a modified back-propagation algorithm, and a linear system called an auto-associative processor (AAP). The standard feature of these algorithms is the use of a set of pre-processed, measured signals to form a system memory. The pre-processed signals used by the AAP algorithm are tutor augmented, while in the case of the FFNN the pre-processed signals are nonlinearly mapped to the training vectors provided by the tutor. The system memory is consequently utilized to process experimental signals to determine some of the AE source properties. [Work partially supported by NSF and ONR.]