Existing FEL facilities often suffer from stability issues: so electron
orbit, transverse electron optics, electron bunch compression and other
parameters have to be readjusted often to account for drifts in performance of
various components. The tuning procedures typically employed in operation are
often manual and lengthy. We have been developing a combination of model-free
and model-based automatic tuning methods to meet the needs of present and
upcoming XFEL facilities. Our approach has been implemented at FLASH
\cite{flash} to achieve automatic SASE tuning using empirical control of orbit,
electron optics and bunch compression. In this paper we describe our approach
to empirical tuning, the software which implements it, and the results of using
it at FLASH. We also discuss the potential of using machine learning and
model-based techniques in tuning methods