  In order to find a fast and reliable method for selecting metal poor galaxies
(MPGs), especially in large surveys and huge database, an Artificial Neural
Network (ANN) method is applied to a sample of star-forming galaxies from the
Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck
Institute and the Johns Hopkins University (MPA/JHU). A two-step approach is
adopted:(i) The ANN network must be trained with a subset of objects that are
known to be either MPGs or MRGs(Metal Rich galaxies), treating the strong
emission line flux measurements as input feature vectors in an n-dimensional
space, where n is the number of strong emission line flux ratios. (ii) After
the network is trained on a sample of star-forming galaxies, remaining galaxies
are classified in the automatic test analysis as either MPGs or MRGs. We
consider several random divisions of the data into training and testing sets:
for instance, for our sample, a total of 70 percent of the data are involved in
training the algorithm, 15 percent are involved in validating the algorithm and
the remaining 15 percent are used for blind testing of the resulting
classifier.
  For target selection, we have achieved an acquisition rate for MPGs of 96
percent and 92 percent for an MPGs threshold of 12+log(O/H)=8.00 and
12+log(O/H)=8.39, respectively.
  Running the code takes minutes in most cases under the Matlab 2013a software
environment.
  The ANN method can easily be extended to any MPGs target selection task when
the physical property of the target can be expressed as a quantitative
variable.
