I would say that if you&#x27;re using a single layer NN, the answer is &quot;you don&#x27;t really know&quot;.  And that gets to a point about how we still don&#x27;t entirely understand <i>how</i> neural networks work, even when they do work.<p>If you were using a deep network though, and if the current theory is correct, it would be a slightly different story.  The current thinking, as I understand it, is that with deep networks, each layer learns representations of certain features (say &quot;slashes&quot;, &quot;edges&quot;, &quot;right slanted lines&quot;, &quot;left slanted lines&quot;, etc.) and the progressively higher layers learn representations composed from those more primitive features.   So if a deep net were recognizing your handwritten characters, you could probably reason that it isn&#x27;t <i>just</i> considering whether the number of black pixels is even or odd.<p>Now in reality this is a pretty contrived, and probably unlikely scenario.  But it&#x27;s a valid question, because there&#x27;s a deeper point to all of this, which involves transference of learning.  That is, how do you take the learning done by a neural network - trained to do one thing - and then leverage that learning in another application.   We still don&#x27;t exactly know how to do that, and that&#x27;s in part because we don&#x27;t entirely understand the nature of the representations the networks build up.   So a very good answer to your question would arguably help understand how to do transference, which would make NN&#x27;s even more useful.