Supervised learning in an online spellchecking service An online spellchecking service Hascheck, today better known for its new website Ispravi.me, continues its trend of rapid growth thanks to the increasing number of total website visits and total number of processed tasks. In doing so, the spellchecker tends to continuously expand and develop its dictionary with all new and unknown but meaningful words it processes through supervised learning. Supervision is necessary to maintain the accuracy of the dictionary, so the entry of each word into the dictionary must be approved by a human. It is desirable to filter out nonsensical and incorrect words as much as possible before they reach manual verification, thus minimizing human effort. This paper offers a programmatic solution for extracting verb forms from the corpus of reported errors. The solution is based on a simple and programmatically feasible classification of verbs in Croatian language by their suffixes, which is comprehensively described and elaborated upon in the chapters of this paper. Alongside the classical programming approach, the paper also provides details for the development of the programmatic solution based on the supervised machine learning method - decision tree. At the end of the paper, the results and effectiveness of the implemented programmatic solution are presented, along with a description of the variable input parameter that can be easily adjusted to the input data set.