Controlling foot-and-mouth disease (FMD) by vaccination requires adequate population coverage and high vaccine efficacy under field conditions. To assure veterinary services that animals have acquired sufficient immunity, strategic post-vaccination surveys can be conducted to monitor the coverage and performance of the vaccine. Correct interpretation of these serological data and an ability to derive exact prevalence estimates of antibody responses requires an awareness of the performance of serological tests. Here, we used Bayesian latent class analysis to evaluate the diagnostic sensitivity and specificity of four tests. A non-structural protein (NSP) ELISA determines vaccine independent antibodies from environmental exposure to FMD virus (FMDV), and three assays measuring total antibodies derived from vaccine antigen or environmental exposure to two serotypes (A, O): the virus neutralisation test (VNT), a solid phase competitive ELISA (SPCE), and a liquid phase blocking ELISA (LPBE). Sera (n = 461) were collected by a strategic post-vaccination monitoring survey in two provinces of Southern Lao People's Democratic Republic (PDR) after a vaccination campaign in early 2017. Not all samples were tested by every assay and each serotype: VNT tested for serotype A and O, whereas SPCE and LPBE tested for serotype O, and only NSP-negative samples were tested by VNT, with 90 of them not tested (missing by study design). These data challenges required informed priors (based on expert opinion) for mitigating possible lack of model identifiability. The vaccination status of each animal, its environmental exposure to FMDV, and the indicator of successful vaccination were treated as latent (unobserved) variables. Posterior median for sensitivity and specificity of all tests were in the range of 92-99 %, except for the sensitivity of NSP (∼66%) and the specificity of LPBE (∼71 %). There was strong evidence that SPCE outperformed LPBE. In addition, the proportion of animals recorded as having been vaccinated that showed a serological immune response was estimated to be in the range of 67-86 %. The Bayesian latent class modelling framework can easily and appropriately impute missing data. It is important to use field study data as diagnostic tests are likely to perform differently on field survey samples compared to samples obtained under controlled conditions.