espanolAntecedentes: la prediccion de la morfologia mandibular es importante tanto en la reconstruccion facial con fines forenses, como en ortodoncia y cirugia maxilofacial. Dicho proceso se ha realizado a traves de metodos parametricos y lineales basandose en poblaciones caucasicas; asimismo, dichos analisis se realizan en radiografias de perfil mas no se tiene en cuenta una prediccion mandibular desde una vista posteroanterior. Proposito: predecir a traves de redes neuronales artificiales la morfologia mandibular, empleando medidas craneomaxilares en radiografias posteroanteriores. Metodos: se recolectaron 229 radiografias posteroanteriores estandarizadas de adultos jovenes colombianos de ambos sexos. Se usaron coordenadas de puntos de referencia oseos craneofaciales para formar medidas mandibulares y craneomaxilares. Se seleccionaron 17 variables predictoras craneomaxilares de entrada, midiendo anchuras, alturas y angulos, De la misma manera se seleccionaron 13 medidas mandibulares a predecir, considerando tanto el lado derecho como el izquierdo. Se usaron redes neuronales artificiales para realizar el proceso de prediccion y se evaluo a traves de un coeficiente de correlacion, por medio de una regresion de arista (ridge regression) entre el valor real y el valor predicho. Resultados: los resultados encontrados dentro del modelo fueron significativos en especial para 5 variables de importancia morfologica dentro del campo forense: la rama mandibular derecha (Cdd-God), el ancho bigoniaco (Goi-God), el ancho bicondilar (Cdi-Cdd) y las distancias entre los condilos al menton (Cdd-Me y Cdi-Me). Conclusion: se encontro una capacidad de prediccion importante en 5 medidas de importancia forense en pacientes Clase I, Clase II y Clase III esqueletica en ambos sexos. EnglishBackground: the prediction of mandibular morphology is important in facial reconstruction for forensic purposes as in orthodontics and maxillofacial surgery. This process has been performed through parametric and linear methods based on Caucasian populations; also, these analyzes are performed on lateral cephalograms, but a prediction from a posteroanterior view is not taken into account. Purpose: to predict through Artificial Neural Networks the mandibular morphology using craniomaxillary measures in posteroanterior radiographs. Methods: 229 standardized posteroanterior radiographs from Colombian young adults of both sexes were collected. Coordinates of craniofacial skeletal landmarks were used to create mandibular and craniomaxillary measures. 17 predictor craniomaxillary input variables were selected, measuring widths, heights and angles. Similarly, 13 mandibular measures were selected to be predicted, considering both the right and left sides. Artificial neural networks were used for the prediction process and it was evaluated by a correlation coefficient using a ridge regression between real value and the predicted value. Results: the results found in the model were significant especially for 5 variables of morphological importance in the forensic field: the right mandibular ramus (Cdd-God), the bigonial width (Goi-God), the bicondylar width (Cdi-Cdd) and the distances between the condyles to the menton (Cdd-Me and Cdi-Me). Conclusions: an important prediction capacity in 5 measures of forensic importance in patients with skeletal Class I, Class II and Class III was found in both sexes.