  With the advent of digital astronomy, new benefits and new challenges have
been presented to the modern day astronomer. No longer can the astronomer rely
on manual processing, instead the profession as a whole has begun to adopt more
advanced computational means. This paper focuses on the construction and
application of a novel time-domain signature extraction methodology and the
development of a supporting supervised pattern classification algorithm for the
identification of variable stars. A methodology for the reduction of stellar
variable observations (time-domain data) into a novel feature space
representation is introduced. The methodology presented will be referred to as
Slotted Symbolic Markov Modeling (SSMM) and has a number of advantages which
will be demonstrated to be beneficial; specifically to the supervised
classification of stellar variables. It will be shown that the methodology
outperformed a baseline standard methodology on a standardized set of stellar
light curve data. The performance on a set of data derived from the LINEAR
dataset will also be shown
