In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.