  In the framework of the European VO-Tech project, we are implementing new
machine learning methods specifically tailored to match the needs of
astronomical data mining. In this paper, we shortly present the methods and
discuss an application to the Sloan Digital Sky Survey public data set. In
particular, we discuss some preliminary results on the 3-D taxonomy of the
nearby (z < 0.5) universe. Using neural networks trained on the available
spectroscopic base of knowledge we derived distance estimates for ca. 30
million galaxies distributed over 8,000 sq. deg. We also use unsupervised
clustering tools to investigate whether it is possible to characterize in broad
morphological bins the nature of each object and produce a reliable list of
candidate AGNs and QSOs.
