Today, obesity is a major public health problem. Obesity increases the risk of diabetes, coronary artery disease, stroke, cancer, premature death and contributes substantially the costs to society. Obesity can be diagnosed with body mass index (BMI). According to the World Health Organization, the diagnosis of overweight is made with a BMI≥ 25 Kg/m2, and obesity with a BMI≥ 30 kg/m2. The diagnosis of obesity has been made using the abdominal circumference, the hip circumference, the thickness of the skin folds and the percentage of body fat (measured directly or indirectly). Besides, the characteristic operating receiver curves (ROC) have been used to find the optimal cut-off points of hip and waist circumference for the diagnosis of obesity. The aim of this study is to evaluate the ability of anthropometric measures for diagnosing overweight and obesity. A database of 1053 subjects with 26 anthropometric measurements was used. For evaluating the predictive ability of anthropometric measures, the area under the ROC curve (AUCROC), the sensitivity (SEN), the specificity (SPE), the negative predictive value (NPV) and the positive predictive value (PPV) were calculated. The hip circumference was the anthropometric value that best detected overweight/obese subjects with a AUCROC= 0.932 (SEN= 0.871, SPE= 0.855, PPV= 0.536 and NPV= 0.972 ) and an optimal cut-off point of 97.2 cm for recognition of obesity. The findings reported in this research suggest that the diagnosis of obesity can be made with anthropometric measurements. In the future, machine learning techniques, such as: k-means, neural networks or support vector machines; will be explored for the detection of overweight and obesity.