A feature extracting method based on wavelets for Fourier Transform Infrared (FTIR) cancer data analysis is presented in this paper. A set of low frequency wavelet basis is used to represent FTIR data to reduce data dimension and remove noise. The fuzzy C-means algorithm is used to classify the data. Experiments are conducted to compare classification performance using wavelet features and the original FTIR data provided by the Derby City General Hospital in the UK. Experiments show that only 30 wavelet features are needed to represent 901 wave numbers of the FTIR data to produce good clustering results.