The control chart is one of the most frequently utilized tools of statistical process control (SPC) in industry to monitor the process variation. The control limits of Shewhart X̄-R control charts are derived under the assumption that the process data are independently and normally distributed. The false alarm may be increased for X̄-R charts when the process data follow a non-normal distribution (e.g., log-normal distribution). The objective of this study is to utilize the non-parametric bootstrap sampling method and two popular bootstrap confidence intervals (i.e., percentile bootstrap (PB) and bias-corrected and accelerated (BCa)) to construct the X̄-R charts for the log-normal distribution. The sensitivity analysis is conducted to verify the effectiveness of the proposed method. The simulation results indicates that for n = 2 to 5, the control limits of the bootstrap X̄-R charts constructed by PB method performs generally better than that of BCa method and Shewhart X̄-R charts in terms of average run length (ARL) for the log-normal distribution.