In industrial quality inspection, it is often the case that a lot of data of desired product appearance can be provided at training time, while very little erroneous examples are available. Thus, in order to train an inspection system, the target appearance has to be learned independently from the availability of defect samples. Defects have to be identified as anomalies wrt the trained data distributions in the online inspection phase. In deep learning, autoencoders are a well known choice to realize anomaly detection scenarios, where significantly larger reconstruction errors of objects’ images indicate defects. However, as the latent code contains enough information to reliably reconstruct good example images, the question arises if a decision about the validity of an input image can already be drawn in that latent space during online inspection. This would speed up the system by more than a factor of 2 by sparing the processing of the autoencoder’s decoder part. Variational Autoencoders (VAE) are a modern variant of the classical autoencoder architecture, which could facilitate this purpose, because of its imposed regularization term, that forces the latent codes to be standard normally distributed.