The aim of this study was to evaluate the applicability of using the growth of the body of C4 vertebra for the estimation of age in children and young adolescents. We used the fact that the proportion between the radiologic projections of the posterior and anterior sides of the C4 vertebral body, which forms a trapezoidal shape, differ with age: in younger individuals, the posterior side is higher, whereas in older individuals, the projections of the sides of the vertebral body form a rectangular shape with the two sides equal or with the anterior side slightly higher. Cephalograms of 444 Italian subjects (214 female and 230 male individuals) aged between 5 and 15 years and with no obvious development abnormalities were analyzed. The projections of the anterior side (a) and of the posterior side (b) of each C4 body were measured and their ratio (Vba), as a value of the C4 body development, was used for age estimation. Distribution of the Vba suggested that it does not change after 13 years in female and 14 years in male subjects. Consequently, we restricted our analysis of the Vba growing model until 14 years in both sexes. We used a Bayesian Calibration method to estimate chronological age as function of Vba as a predicting variable. The intra- and inter- observer agreement was satisfactory, using intra-class correlation coefficient of Vba on 30 randomly selected cephalograms. The mean absolute errors were 1.34 years (standard deviation 0.95) and 1.01 years (standard deviation 0.71) and the mean interquartile ranges of the calibrating distribution were 2.32 years (standard deviation 0.25) in male and 1.72 years (standard deviation 0.39) in female individuals, respectively. The slopes of the regression of the estimated age error to chronological age were 0.02 in male and 0.06 in female individuals, where both values did not result significantly different from 0 (p>0.12). In conclusion, although our Bayesian calibration method might not really outperform the classical regression models in the precision of its estimates, it appears to be more robust, to greatly reduce the typical bias inherent in the regression model approach, and incorporate multiple predictors.