  We present an automatic, fast, accurate and robust method of classifying
astronomical objects. The Self Organizing Map (SOM) as an unsupervised
Artificial Neural Network (ANN) algorithm is used for classification of stellar
spectra of stars. The SOM is used to make clusters of different spectral
classes of Jacoby, Hunter and Christian (JHC) library. This ANN technique needs
no training examples and the stellar spectral data sets are directly fed to the
network for the classification. The JHC library contains 161 spectra out of
which, 158 spectra are selected for the classification. These 158 spectra are
input vectors to the network and mapped into a two dimensional output grid. The
input vectors close to each other are mapped into the same or neighboring
neurons in the output space. So, the similar objects are making clusters in the
output map and making it easy to analyze high dimensional data.
  After running the SOM algorithm on 158 stellar spectra, with 2799 data points
each, the output map is analyzed and found that, there are 7 clusters in the
output map corresponding to O to M stellar type. But, there are 12
misclassifications out of 158 and all of them are misclassified into the
neighborhood of correct clusters which gives a success rate of about 92.4%.
