There is growing interest in the development of prognostic models for predicting the occurrence of acute graft-vs-host disease (aGVHD) after unrelated donor hematopoietic stem cell transplantation. A high number of variables have been shown to play a role in aGVHD, but the search for a predictive algorithm is still ongoing. Artificial neural networks (ANNs) represent an attractive alternative to multivariate analysis for clinical prognosis. So far, no reports have investigated the ability of ANNs in predicting HSCT outcome.We compared the prognostic performance of ANNs with that of logistic regression (LR) in 78 beta-thalassemia major patients given unrelated donor hematopoietic stem cell transplantation. Twenty-four independent variables were analyzed for their potential impact on outcomes.Twenty-six patients (33.3\%) developed grade II to IV aGVHD. In multivariate analysis, homozygosity for donor KIR haplotype A (p = 0.03), donor age (p = 0.05), and donor homozygosity for the deletion of the human leukocyte antigen-G 14-bp polymorphism (p = 0.05) were independently significantly correlated to aGVHD. The mean sensitivity of LR and ANNs (capability of predicting aGVHD in patients who developed aGVHD) in test datasets was 21.7\% and 83.3\%, respectively (p