The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical–and potentially structural–properties. However, technical and biological reasons, including–but not limited to–the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF. More sophisticated methods of analyzing the ECG signal, on a single- or multi- beat basis, along with novel approaches to data handling, namely machine learning, seem to be leading up to more accurate and robust ways to obtain clinically useful information from the humble P wave.