  We introduce ANNz, a freely available software package for photometric
redshift estimation using Artificial Neural Networks. ANNz learns the relation
between photometry and redshift from an appropriate training set of galaxies
for which the redshift is already known. Where a large and representative
training set is available ANNz is a highly competitive tool when compared with
traditional template-fitting methods.
  The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release
1, and for this particular data set the r.m.s. redshift error in the range 0 <
z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or
which are brighter than the photometric set for which redshifts are required)
are simulated and the impact on the photometric redshift accuracy assessed.
