Good classification of human brain tumours based on ¹H NMR spectra of biopsy extracts were be obtained using a genetic programming (GP) approach. In addition, the most significant aspect of the analysis was that very simple functions gave classification results that were almost as good as the `best-ever' functions. The results from classification using GP are unclear. GP copes very well with the binary classification on brain tumours where the data is noisy, due to, e.g. diverse tumour types and uncertainties in histological classification. GP performs at least as well as NN, and finds solutions that are simple. On the multi-class rat data, where the data is more homogenous, GP performs much less well than NN. Only when some extra preprocessing is applied to the data does GP perform as well as the results from the human brain classification would lead one to expect. (3 pages)