This paper analyses the effect of training samples on supervised classification of remote sensing data, proposes a theory and method for purincation of training samples, which uses spectral and spatial information to remove the undesirable sample pixels. An example shows that divergence between classes, goodness of fit to Gaussian distributinn and classification accuracy can be improved after purification of training samples.