In this article, the application of the scanning n-tuple technique to classification tasks is studied. The performance of this technique is examined in a handwritten character recognition task where the accuracy is initially low. This task is employed as a case study for designing a general-purpose algorithm that improves the scanning n-tuple performance in hard classification tasks, by focusing on the characteristics of the pattern space. Experimental results indicate that the use of the algorithm results in a substantial improvement of the scanning n-tuple classification performance in comparison to previous results. This improvement is shown to be equivalent to that achieved by employing structural knowledge regarding the specific pattern space