Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstrap control charts are two remedial measures that we could use to overcome this issue. In this paper, a new bootstrap algorithm is proposed to construct Hotelling’s T2 control chart. The performance of proposed chart is evaluated through a simulation study. Our results are compared to the traditional Hotelling’s T2 control chart results and the bootstrap results reported by Phaladiganon et al.  using in-control and out-of-control average run lengths denoted by ARL0 and ARL1, respectively. The latter case is obtained when the process mean is subject to sustained shifts. Numerical results indicate that the proposed algorithm performs better than the above mentioned methods. The new bootstrap algorithm is also applied to a real data set.