Abstract LogAnswer is a question answering (QA) system for the German language, aimed at providing concise and correct answers to arbitrary questions. For this purpose LogAnswer is designed as an embedded arti?cial intelligence system which integrates methods from several ?elds of AI, namely natural language processing, machine learning, knowledge representation and automated theorem proving. We intend to employ LogAnswer as a virtual user within Internet-based QA forums, where it must be able to identify the questions that it cannot answer correctly, a task that normally receives little attention in QA research compared to the actual answer derivation. The paper presents a machine learning solution to the wrong answer avoidance (WAA) problem, applying a meta classi?er to the output of simple term-based classi?ers and a rich set of other WAA features. Experiments with a large set of real-world questions from a QA forum show that the proposed method signi?cantly improves the WAA characteristics of our system.