  Daily operation of a large-scale experiment is a challenging task,
particularly from perspectives of routine monitoring of quality for data being
taken. We describe an approach that uses Machine Learning for the automated
system to monitor data quality, which is based on partial use of data qualified
manually by detector experts. The system automatically classifies marginal
cases: both of good an bad data, and use human expert decision to classify
remaining "grey area" cases.
  This study uses collision data collected by the CMS experiment at LHC in
2010. We demonstrate that proposed workflow is able to automatically process at
least 20\% of samples without noticeable degradation of the result.
