World Wide Web content continuously grows in size and importance. Furthermore, users ask Web search engines to satisfy increasingly disparate information needs. New techniques and tools are constantly developed aimed at assisting users in the interaction with the Web search engine. Query recommender systems suggesting interesting queries to users are an example of such tools. Most query recommendation techniques are based on the knowledge of the behaviors of past users of the search engine recorded in query logs. A recent query-log mining approach for query recommendation is based on Query Flow Graphs (QFG). In this paper we propose an evaluation of the effects of time on this query recommendation model. As users interests change over time, the knowledge extracted from query logs may suffer an aging effect as new interesting topics appear. In order to validate experimentally this hypothesis, we build different query flow graphs from the queries belonging to a large query log of a real-world search engine. Each query flow graph is built on distinct query log segments. Then, we generate recommendations on different sets of queries. Results are assessed both by means of human judgments and by using an automatic evaluator showing that the models inexorably age.