Many applications, e.g., fault detection, quality of industrial process, monitoring and prediction of climatic phenomena assume the stationary hypothesis or require identification of the process change. Change detection tests satisfy the change detection necessity by identifying a drift, a different expected behavior, a deviation; their effectiveness is generally based on statistical confidence tests whose parameters are configured at design-time (generally through a trial-and-error approach). Here, we suggest an extension of the widely-used CUSUM change detection test which improves effectiveness and timeliness in detecting changes by adaptively configuring its test parameters