Abstract Acoustic emission (AE) analysis is used for characterization and location of developing defects in materials. The location of AE on complicated aircraft frame structures is a difficult problem of non-destructive testing. This article describes an intelligent AE source locator which comprises a sensor antenna and a general regression neural network, which solves the location problem based on learning from examples. The location accuracy achieved by the intelligent locator is comparable to that obtained by the conventional triangulation method, while the applicability of the intelligent locator is more general since analysis of sonic ray paths is avoided. AE sources often generate a mixture of various statistically independent signals. A difficult problem of AE analysis is separation and characterization of signal components when the signals from various sources and the mode of mixing are unknown. Recently, blind source separation (BSS) by independent component analysis (ICA) has been used to solve these problems. The purpose of this paper is to demonstrate the applicability of ICA to separate and locate two independent simultaneously active AE sources on an aluminum band specimen. The method is promising for non-destructive testing of aircraft frame structures by acoustic emission analysis.