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The paper presents and evaluates an efficient algorithm for measuring semantic similarity of texts. Calculating the level of semantic similarity of texts is a very difficult task and the proposed up to now methods suffer from computational complexity. This substantially limits their application area. The proposed algorithm tries to reduce the problem by merging a computationally efficient statistical approach to text analysis with a semantic component. The semantic properties of text words are extracted from the WordNet lexical database. The approach was tested using WordNets for two languages: English and Polish. The basic properties of this approach are also studied. The paper concludes with an analysis of the performance of the proposed method on a sample database and suggests some possible application areas.