  We propose a novel method for analyzing precursory seismic data before an
earthquake that treats them as a Markov process and distinguishes the
background noise from real fluctuations due to an earthquake. A short time (on
the order of several hours) before an earthquake the Markov time scale $t_M$
increases sharply, hence providing an alarm for an impending earthquake. To
distinguish a false alarm from a reliable one, we compute a second quantity,
$T_1$, based on the concept of extended self-similarity of the data. $T_1$ also
changes strongly before an earthquake occurs. An alarm is accepted if {\it
both} $t_M$ and $T_1$ indicate it {\it simultaneously}. Calibrating the method
with the data for one region provides a tool for predicting an impending
earthquake within that region. Our analysis of the data for a large number of
earthquakes indicate an essentially zero rate of failure for the method.
