  Analysis of the data shows that hadron tags of the two standard DELPHI
particle identification packages RIBMEAN and HADSIGN are weakly correlated.
This led to the idea of constructing a neural network for both kaon and proton
identification using as input the existing tags from RIBMEAN and HADSIGN, as
well as preproccessed TPC and RICH detector measurements together with
additional dE/dx information from the DELPHI vertex detector. It will be shown
in this note that the net output is much more efficient at the same purity than
the HADSIGN or RIBMEAN tags alone. We present an easy-to-use routine performing
the necessary calculations.
