Soft-biometrics prediction from handwriting analysis is gaining a wide interest in writer identification since it gives additional knowledge about the writer like its gender (man or woman), its handedness (left-handed or right-handed) and its age range. All research works developed in this context were focused on predicting a single soft-biometric trait. Nevertheless, it could be more interesting to develop a system that predicts several traits from a handwritten text. Presently, we investigate the feasibility of such multiple trait prediction. To reach this end, we propose two prediction schemes. The first combines individual prediction scores to aggregate a global prediction. The second scheme is based on a multi-class prediction. For both schemes, the prediction is based on SVM classifier associated with Gradient features. Experimental corpus is collected from IAM handwritten database. Conclusively, the second scheme proved to be more promising and evinced that the age characteristic is stable over time for a certain category of writers.