We show how to introduce a characterization the ''complexity'' of rand
om dynamical systems. More precisely we propose a suitable indicator o
f complexity in terms of the average number of bits per time unit nece
ssary to specify the sequence generated by these systems. This indicat
or of complexity, which can be extracted from real experimental data,
turns out to be very natural in the context of information theory. For
dynamical systems with random perturbations, it coincides with the ra
te K of divergence of nearby trajectories evolving under two different
noise realizations. In presence of strong dynamical intermittency, th
e value of K is very different from the standard Lyapunov exponent chi
(sigma) computed through the consideration of two nearby trajectories
evolving under the same realization of the random perturbation. Howeve
r, the former is much more relevant than the latter from a physical po
int of view as illustrated by some numerical examples of noisy and ran
dom maps.