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We give a brief overview of artificial neural networks (ANNs), focusing on Kohonen networks (KNs). The two kinds of KNs will be described in detail: the unsupervised self-organizing map (SOM) and the supervised learning vector quantization (LVQ). We then apply these algorithms to two astronomical classification problems: the classification of broad absorption line quasars (BALQSOs) and of gamma-ray bursts (GRBs). In the context of BALQSOs, we find a BALQSO fraction of 10.4%, and compile a catalogue from the Sloan Digital Sky Survey (SDSS) using the supervised LVQ. This is currently the most complete BALQSO catalogue. We then apply the unsupervised SOM to GRB light curves obtained from the Burst and Transient Source Experiment (BATSE). Using only shape-dependent variables, we find that two classes are recovered: single-pulsed bursts (SPBs) and multi-pulsed bursts (MPBs). We show that these two network classes also have different observational properties that are independent of light curve shape (T90 and fluence), suggesting an intrinsic difference between the two. We conclude with some attempts to correlate our GRB result to previous studies and suggest improvements for future work

Astronomical Data Mining with Neural Networks