  Quantifying how distinguishable two stochastic processes are lies at the
heart of many fields, such as machine learning and quantitative finance. While
several measures have been proposed for this task, none have universal
applicability and ease of use. In this Letter, we suggest a set of requirements
for a well-behaved measure of process distinguishability. Moreover, we propose
a family of measures, called divergence rates, that satisfy all of these
requirements. Focussing on a particular member of this family -- the
co-emission divergence rate -- we show that it can be computed efficiently,
behaves qualitatively similar to other commonly-used measures in their regimes
of applicability, and remains well-behaved in scenarios where other measures
break down.
