In this paper a feedforward neural network architecture 
to model survival probabilities is illustrated.
The two main features of the network are that non-linearity 
are captured in the survival function, and the time variable 
is embedded in the model so it is able to extract its interactions 
with other system features. The model is described in a hierarchical 
Bayesian framework. Some experiments with synthetic and real world 
data show the capabilities of the model.