Programs often emit natural language messages, e.g., in logging statements or exceptions raised on unexpected paths. To be meaningful to users and developers, the message, i.e., \emph{what} to say, must be consistent with the condition under which it gets trigger, i.e., \emph{when} to say it. However, checking for inconsistencies between conditions and messages is challenging because the conditions are expressed in the logic of the programming language, while messages are informally expressed in natural language. This paper presents CMI-Finder, an approach for detecting \emph{condition-message inconsistencies}. CMI-Finder is based on a neural model that takes a condition and a message as its input and then predicts whether the two are consistent. To address the problem of obtaining realistic, diverse, and large-scale training data, we present six techniques to generate large numbers of inconsistent examples to learn from automatically. Moreover, we describe and compare three neural models, which are based on binary classification, triplet loss, and fine-tuning, respectively. Our evaluation applies the approach to 300K condition-message statements extracted from 42 million lines of Python code. The best model achieves a precision of 78% at a recall of 72% on a dataset of past bug fixes. Applying the approach to the newest versions of popular open-source projects reveals 50 previously unknown bugs, eight of which have been confirmed by the developers so far.