Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatio-temporal spike pattern. By using delays that complement the firing times in a pattern, spikes can arrive simultaneously at an output neuron, giving it a high chance of firing in response to that pattern. An unsupervised learning mechanism called spike-timing-dependent plasticity then increases the weights for connections used in the pattern, and decreases the others. This allows for an attunement of output neurons to specific activity patterns, based on temporal aspects of axonal conductivity.