With the increasing amount of text files that are produced nowadays, spell checkers have become essential tools for everyday tasks of millions of end users. Among the years, several tools have been designed that show decent performances. Of course, grammatical checkers may improve corrections of texts, nevertheless, this requires large resources. We think that basic spell checking may be improved (a step towards) using the Web as a corpus and taking into account the context of words that are identified as potential misspellings. We propose to use the Google search engine and some machine learning techniques, in order to design a flexible and dynamic spell checker that may evolve among the time with new linguistic features.