Web search engines have major impact in people's everyday life. It is of great importance to test the retrieval effectiveness of search engines. However, it is labor-intensive to judge the relevance of search results for a large number of queries, and these relevance judgments may not be reusable since the Web data change all the time. In this work, we propose to mine test oracles of Web search engines from existing search results. The main idea is to mine implicit relationships between queries and search results, e.g., some queries may have fixed top 1 result while some may not, and some Web domains may appear together in top 10 results. We define a set of items of queries and search results, and mine frequent association rules between these items as test oracles. Experiments on major search engines show that our approach mines many high-confidence rules that help understand search engines and detect suspicious search results.