  In this paper we consider a stochastic heavy-ball method for solving linear
ill-posed inverse problems. With suitable choices of the step-sizes and the
momentum coefficients, we establish the regularization property of the method
under {\it a priori} selection of the stopping index and derive the rate of
convergence under a benchmark source condition on the sought solution.
Numerical results are provided to test the performance of the method.
