This work proposes an EWMA process control chart for individual observations or subgroup averages, in which the smoothing constant varies between two values according to the most recent value of the EWMA statistic, in order to achieve faster detection of small to moderate shifts in the process mean, and without the operational complexities presented by other adaptive schemes, since its sample size and sampling interval do not vary. There is one other work proposing the adaptive variation of the smoothing constant of EWMA charts, but based on a different criterion: Capizzi and Masarotto (2003). The adaptive EWMA scheme was combined with Shewhart limits for the individual values (or subgroup averages), to enhance its sensitivity to large shifts, again with no extra operational burden. The out-of-control average run lengths (ARL1´s) were calculated through a numerical approximation method based on a Markov chain model. The ARL1´s were compared of the proposed scheme, of the traditional (fixed parameter) EWMA chart and of Capizzi and Masarottos´s adaptive EWMA scheme. The proposed scheme generally provides the shortest ARL1´s for shifts in the mean above one standard deviation, and Capizzi and Masarotto´s scheme tends to outperform it for smaller shifts. Both schemes perform better than the fixed parameter EWMA. An advantage that can become decisive for the adoption of the proposed scheme is the simplicity of the calculations required for the monitoring.