In this work we show a framework for guiding the classical constraint programming resolution process. Such a framework allows one to measure the resolution process state in order to perform an "on the fly"replacement of strategies exhibiting poor performances. The replacement is performed depending on a quality rank, which is computed by means of a choice function. The choice function determines the performance of a given strategy in a given amount of time through a set of indicators and control parameters. The goal is to select promising strategies to achieve efficient resolution processes. The main novelty of our approach is that we reconfigure the search based solely on performance data gathered while solving the current problem. We report encouraging results where our combination of strategies outperforms the use of individual strategies. © 2011 Springer-Verlag Berlin Heidelberg.