Abstract: A method and apparatus for analysing a sample, in which a neural network is trained to correct for measurement drift of a given analytical instrument (e.g., a mass spectrometer). The training is carried out using first and second sets of data obtained by the instrument from samples of known compositions at initial and subsequent instants of time, respectively. The trained neural network is used to transform data, obtained by the instrument from a sample of unknown composition at said subsequent instant of time, to an estimate of the data which would have been obtained by the instrument from that sample at the initial instant of time. The transformed dasta is then analysed to analyse the sample of unknown composition.