Pattern classification algorithms can help us to recognize and predict patterns in large and complex multivariate datasets. Self-organizing maps (SOMs), which use competitive learning, are unsupervised classification tools that are considered to be very useful in pattern classification and recognition. This technique is based on the principles of vector quantification of similarities and clustering in a high-dimensional space and the method can handle the analysis and visualization of high-dimensional data. This tool is ideal for analysing a complex combination of categorical and continuous spatial variables, with particular applications to geological features. We used SOMs to predict geological features based on airborne geophysical data acquired through the Tellus Project in Northern Ireland. The SOMs were applied through 20 experiments (iterations), incorporating radiometric data in combination with geological features, including elevation, slope angle, terrain ruggedness and geochronology. The SOMs were able to differentiate contrasting bedrock geology, such as acidic v. mafic igneous rocks, although data clustering over intermediate rocks was less clear. The presence of a thick cover of glacial deposits in most of the study area presented a challenge for data clustering, particularly over the intermediate igneous and sedimentary bedrock types.