Two obstacles in bioprocess monitoring are lack of reliable measurements of key process variables and difficulty in defining quantitative relationships between microbial growth rate and the accessible process state variables. A case study on Saccharomyces Cerevisiae production is used to demonstrate an approach based on Kalman filtering and use of a hybrid process model combining differential balance equations, to model the main system dynamics, with Artificial Neural Networks (ANNs), to model the specific growth rates.