A problem of stochastic optimization with joint chance constraints is approximated by a problem of conditional value at risk which is attacked by the method of sample average approximation. The authors prove that under moderate conditions the optimal solutions and stationary points, obtained by applying the sample average approximation method, converge with probability one to their true counterparts. The exponential convergence rate is established for the convergence of stationary points. Similar convergence results for DC-approximation of chance constraints are listed where DC means an approximation by the difference of two convex functions. The results of numerical experiments are reported.