In order to handle the vast amount of information collected by JET diagnostics, which can exceed 10 Gbytes of data per shot, a series of new soft computing methods are being developed. They cover various aspects of the data analysis process, ranging from information retrieval to statistical confidence and machine learning. In this paper some recent developments are described. History effects in the plasma evolution leading to disruptions have been investigated with the use of Artificial Neural Networks. New image processing algorithms, based on optical flow techniques, are being used to derive quantitative information about the movement of objects like filaments at the edge of JET plasmas. Adaptive filters, mainly of the Kalman type, have been successfully implemented for the online filtering of MSE data for real time purposes.