Items that a user can see when he uses the general result page of a modern search engine can be categorized as verticals. Some examples of verticals are images, videos, news, shopping. Heterogeneous search engine result pages encompass result pages that contain results from different verticals. It is widely used and has been proven to improve the user experience over the result pages that only contain a list of websites. Different verticals are appropriate for each query. We study how to define, develop, and evaluate a vertical selection model, that for a query selects and presents the appropriate verticals. We give an approach for collecting a corpus of documents that represent different verticals. Later corpus documents are used as training data for query result classification. Features were extracted from the documents to train a classifier. The model that uses the Random Forest classifier and features extracted from the query itself achieved an f-score of 0.4921 on the TREC 2014 dataset. The score and the analysis of the results show that the proposed vertical selection methodology is viable. To better capture the difference between documents in different verticals, the corpus collection approach should be improved.