Introduction : The health status of women in peri-urban areas has been influence by the South African political transition. Despite some progress, maternal and child mortality rates are still unacceptably high. A mother’s nutritional status is one of the most important determinants of maternal and birth outcomes. The Institute of Medicine’s pre-pregnancy Body Mass Index (BMI) method is not always appropriate to use in a peri-urban setting as many women attend their first antenatal clinic later on in their pregnancy. Two alternative methods, the gestational BMI (GBMI) and the gestational risk score (GRS), have been used elsewhere to screen for at risk pregnancies, but have not been used in a South African peri-urban setting. Furthermore, examining socio-economic variables (SEV) aids in the explanation of the impact of social structures on an individual. Risk factors can then be established and pregnant women in these higher risk groups can be identified and given additional antenatal clinic appointments and priority during labour. Aim: The first aim was to investigate the strength of the GBMI and GRS methods for predicting birth outcomes and maternal morbidities. The second aim was to investigate the relationships between SEV, GBMI and maternal morbidities. Methods: This was a sub-study of the Philani Mentor Mothers Study. A sample of 103 and 205 were selected for investigating the prediction methods and SEV respectively. Maternal anthropometry, gestational weeks and SEV were obtained during interviews before birth. Information obtained was used to calculate GBMI and GRS and to assess the SEV. Birth outcomes were obtained from the infant’s clinic cards and maternal morbidities were obtained from interviews two days after the birth. Results No significant association was found between GBMI and birth outcomes and maternal morbidities. A significant positive association was found between GRS and birth head circumference percentile (r=0.22, p<0.05). The higher the GRS, the higher the risk of an infant spending longer time in the hospital (Kruskal Wallis X2 = 4, p<0.05). A significant positive association was found between GBMI and the following SEV factors; age (r=0.33, p<0.05), height (r=0.15, p<0.05), parity (r=0.23, p<0.05), income (r=0.2, p<0.05), marital status (X2 = 9.35, p<0.05), employment (U=2.9, p<0.05) and HIV status (U=2.54, p<0.05). No statistically significant relationships were found between gestational hypertension and gestational diabetes mellitus and SEV. Conclusion: From the findings of this sub-study there were some promising results, however it is still unclear as to which method is the most appropriate to predict adverse birth outcomes and maternal morbidity. It is recommended that the GBMI and GRS once-off methods be repeated in a larger population to see if there are more parameters that could be predicted. Women who were older, shorter, married, had more pregnancies, HIV negative and had a higher socioeconomic status tended to have a greater GBMI. This can lead to adverse birth outcomes and increases the risk of women developing maternal morbidities and other chronic diseases later in their life. Optimal nutrition and health promotion strategies targeting women before conception should be implemented.