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The adoption of Artificial Neural Networks (ANNs) in safety-related applications is often avoided because it is difficult to rule out possible misbehaviors with traditional analytical or probabilistic techniques. In this paper we present N eV er, our tool for checking safety of ANNs. N eV er encodes the problem of verifying safety of ANNs into the problem of satisfying corresponding Boolean combinations of linear arithmetic constraints. We describe the main verification algorithm and the structure of N eV er. We present also empirical results confirming the effectiveness of N eV er on realistic case studies. [ABSTRACT FROM AUTHOR]