An automatic analysis method comprising: a multivariate curve resolution executing step of executing multivariate curve resolution (S103) based on spectra detected at a plurality of measurement points from a same sample and resulting in a measurement data matrix (D), using a provisional number of components as a number of components included in the sample so as to obtain resolution spectral data whose number of pieces is the same as the provisional number of components; a component discriminating step (S105) of identifying components in the sample based on the respective pieces of resolution spectral data obtained by the multivariate curve resolution, wherein each piece of resolution spectral data is identified as corresponding to a respective component in the sample so that the components are identified; wherein if the components identified as being in the sample in the component discriminating step are different to each other, such that all pieces of resolution spectra identified as corresponding to a given component are different from all pieces of resolution spectra identified as corresponding to a different component, then the multivariate curve resolution executing step and the component discriminating step are repeated (S108) with the provisional number of components increased by one for each repetition until at least two of the components identified as being in the sample in the component discriminating step are matched in at least two pieces amongst said pieces of resolution spectra; and a component number determining step (S113) of determining a number of the components included in the sample as a value obtained by subtracting one from the provisional number of components in use at the time when at least two of the components identified as being in the sample in the component discriminating step are matched to each other. a multivariate curve resolution executing step of executing multivariate curve resolution (S103) based on spectra detected at a plurality of measurement points from a same sample and resulting in a measurement data matrix (D), using a provisional number of components as a number of components included in the sample so as to obtain resolution spectral data whose number of pieces is the same as the provisional number of components; a component discriminating step (S105) of identifying components in the sample based on the respective pieces of resolution spectral data obtained by the multivariate curve resolution, wherein each piece of resolution spectral data is identified as corresponding to a respective component in the sample so that the components are identified; wherein if the components identified as being in the sample in the component discriminating step are different to each other, such that all pieces of resolution spectra identified as corresponding to a given component are different from all pieces of resolution spectra identified as corresponding to a different component, then the multivariate curve resolution executing step and the component discriminating step are repeated (S108) with the provisional number of components increased by one for each repetition until at least two of the components identified as being in the sample in the component discriminating step are matched in at least two pieces amongst said pieces of resolution spectra; and a component number determining step (S113) of determining a number of the components included in the sample as a value obtained by subtracting one from the provisional number of components in use at the time when at least two of the components identified as being in the sample in the component discriminating step are matched to each other. <EOS>