An artificial neural network (ANN) modeling scheme has been constructed for the identification of both recombinant tissue-type plasminogen activator (r-tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed-forward backpropagation neural networks were constructed to function as a software sensor. This enabled predictions of viable cell density, r-tPA content, and r-tPA glycosylation. The sensor was based on an initial input vector space consisting of simple metabolite concentrations, batch cultivation time, and a description of shear stress applied to the culture. Metabolite concentrations of the culture supernatant, included in the input vector space, were obtained from a single isocratic HPLC measurement. The shear stress component of the input space enabled accurate culture state prediction over a wide range of agitation rates. Coefficient of determination (r(2)) values between ANN predicted and experimental measurements of 0.945, 0.943, 0.956, and 0.990 were calculated to validate individual ANN prediction accuracy for total ammonia, apparent viable cell density, total r-tPA, and Type II glycoform concentrations, respectively.