  This paper tackles the feedback stabilization of switched linear systems
under arbitrary switching. We propose a data-driven approach which allows to
compute a stabilizing static feedback using only a finite set of observations
of trajectories without any knowledge of the dynamics. We assume that the
switching signal is not observed, and as a consequence, we aim at solving a
\emph{uniform} stabilization problem in which the feedback is stabilizing for
all possible switching sequences. In order to generalize the solution obtained
from trajectories to the actual system, probabilistic guarantees are derived
via geometric analysis in the spirit of scenario optimization. The performance
of this approach is demonstrated on a few numerical examples.
