Mutations that affect splicing of precursor messenger RNAs play a major role in the development of hereditary diseases. Most splicing mutations have been found to eliminate GT or AG dinucleotides that define the 5' and 3' ends of introns, leading to exon skipping or cryptic splice-site activation. Although accurate description of the mis-spliced transcripts is critical for predicting phenotypic consequences of these alterations, their exact nature in affected individuals cannot often be determined experimentally. Using a comprehensive collection of exons that sustained cryptic splice-site activation or were skipped as a result of splice-site mutations, we have developed a multivariate logistic discrimination procedure that distinguishes the two aberrant splicing outcomes from DNA sequences. The new algorithm was validated using an independent sample of exons and implemented as a free online utility termed CRYP-SKIP (http://www.dbass.org.uk/cryp-skip/). The web application takes up one or more mutated alleles, each consisting of one exon and flanking intronic sequences, and provides a list of important predictor variables and their values, the overall probability of activating cryptic splice vs exon skipping, and the location and intrinsic strength of predicted cryptic splice sites in the input sequence. These results will facilitate phenotypic prediction of splicing mutations and provide further insights into splicing enhancer and silencer elements and their relative importance for splice-site selection in vivo.