Learning the meaning of words is a complicated task with many problems. In this study an algorithm to map words to meanings was developed regarding the three problems: handling of sentences (not only singular words), distinguishing the correct of multiple events in a scene and building a lexicon with no entries at the beginning. The aim of this study was to implement an algorithm that would replicate the results of a previous study. The results acquired confirmed the work previously done, the same percentage of word meanings (100%) were learned with equal conditions. To further develop the algorithm problems with words that are spelled identically but mean different things and contexts where events are not describing the utterances said need to be solved. This would make the algorithm more applicable in real world situations.