  We present a new analysis of the Sloan Digital Sky Survey data aimed at
producing a detailed map of the nearby (z < 0.5) universe. Using neural
networks trained on the available spectroscopic base of knowledge we derived
distance estimates for about 30 million galaxies distributed over ca. 8,000 sq.
deg. We also used unsupervised clustering tools developed in the framework of
the VO-Tech project, to investigate the possibility to understand the nature of
each object present in the field and, in particular, to produce a list of
candidate AGNs and QSOs.
