  Thanks to the advances in robotic telescopes, the time domain astronomy leads
to a large number of transient events detected in images every night. Data
mining and machine learning tools used for object classification are presented.
The goal is to automatically classify transient events for both further
follow-up by a larger telescope and for statistical studies of transient
events. A special attention is given to the identification of gamma-ray burst
afterglows. Machine learning techniques is used to identify GROND gamma-ray
burst afterglow among the astrophysical objects present in the SDSS archival
images based on the $g'-r'$, $r'-i'$ and $i'-z'$ colour indices. The
performance of the support vector machine, random forest and neural network
algorithms is compared. A joint meta-classifier, built on top of the individual
classifiers, can identify GRB afterglows with the overall accuracy of $\gtrsim
90\%$.
