The triple test is a screening test (blood test) used to calculate the probability of a pregnant woman having a fetus that has a chromosomal abnormality like Down Syndrome (DS). AFP (Alpha-Fetoprotein), hCG (Human Chorionic Gonadotropin), and uE3 (Unconjugated Estriol) values in the blood sample of pregnant women are computed and compared with the similar real records where the outputs (healthy fetus or a fetus with DS) are actually known. The likelihood of the indicators is used to calculate the probability of having a fetus with chromosomal abnormality like DS. However, high false positive rate of the triple test has been a problematic issue. One of the reasons of the high false positives is the differences in the norm values of indicators for the pregnant women from different geographical regions of a country. We use 81 patient records retrieved from Sahinbey Training and Research Hospital of Gaziantep University; Turkey. In our study, nine different classification algorithms were trained based on triple test indicators. Multilayer perceptron outperformed with 94.24% detection rate and 13% false positive rate. The multilayer perceptron can predict the outcome of triple test with a high level of accuracy and fewer patients are suggested for amniocentesis. This study is the first study using the MLP model for Turkish triple test data. Regional MLP models can eliminate the bias due to local biological differences.