Fetal biometry is considered the keystone in fetal well-being assessment. In particular, fetal growth curves built by means of ultrasound images and reference charts (defining the normal and pathological sizes for each biometric parameter and for each gestational age) are extensively adopted to track fetal sizes from the early phases of pregnancy up to delivery. In literature a large variety of reference charts are reported to consider the differences among different ethnic groups, but they are up to five decades old and they do not consider environmental factors such as foods, lifestyle, smoke, familial aspects, physiological and pathological variables, temporal parameters etc., which cannot be disregarded in a correct diagnosis. Therefore, current reference charts are rapidly becoming inadequate to support the melting pot of ethnic groups and lifestyles of our society, while customized reference charts can provide an accurate fetal assessment for the different fetal anthropometrical variables. Starting from a detailed analysis of the limits of classical reference charts, the paper presents a new method, based on multidimensional analysis for creating personalized fetal growth curves. A simple implementation, based on Open Source software and simulated data, shows the need of Big Data techniques in order to scale up the problem.