With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.