A Bayesian model was developed to estimate values for the prevalence and diagnostic test characteristics of bovine cysticercosis (Taenia saginata) by combining results of four imperfect tests. Samples of 612 bovine carcases that were found negative for cysticercosis during routine meat inspection collected at three Belgian slaughterhouses, underwent enhanced meat inspection (additional incisions in the heart), dissection of the predilection sites, B158/B60 Ag-ELISA and ES Ab-ELISA. This Bayesian approach allows for the combination of prior expert opinion with experimental data to estimate the true prevalence of bovine cysticercosis in the absence of a gold standard test. A first model (based on a multinomial distribution and including all possible interactions between the individual tests) required estimation of 31 parameters, while only allowing for 15 parameters to be estimated. Including prior expert information about specificity and sensitivity resulted in an optimal model with a reduction of the number of parameters to be estimated to 8. The estimated bovine cysticercosis prevalence was 33.9% (95% credibility interval: 27.7-44.4%), while apparent prevalence based on meat inspection is only 0.23%. The test performances were estimated as follows (sensitivity (Se) - specificity (Sp)): enhanced meat inspection (Se 2.87% - Sp 100%), dissection of predilection sites (Se 69.8% - Sp 100%), Ag-ELISA (Se 26.9% - Sp 99.4%), Ab-ELISA (Se 13.8% - Sp 92.9%).