Norm Optimal Iterative Learning Control is formulated and illustrated by applications to discrete and continuous state-space systems. Convergence conditions and other properties are established. Frequency attenuation and eigenstructure interpretations are derived and some insight into parameter choice is revealed. Robustness conditions are put forward and written in frequency domain terms for discrete state-space systems. Issues that affect algorithm performance are discussed.