<p>Worldwide, suicide is considered a health problem where adolescents are most at risk. This work developed an assessment and prognosis method for the early diagnosis of suicide risk in school adolescents through multivariate cluster analysis techniques and artificial neural networks. Variables related to suicidal thoughts, plans and manifestations were analyzed in (n=638) adolescents in a public school. Cluster analysis identified 73.2% of adolescents with low suicide risk, 18.5% with medium risk and 8.3% with high risk. A neural network was designed with a 95.5% correct classification capacity. It was concluded that the systematization of the method that integrates a cluster analysis and an artificial neuronal network has the capacity to discriminate and diagnose early suicidal risk in schooled adolescents.</p>