The production of customized goods is becoming more and more important for industrial companies. The large number of variants resulting from this, up to batch size 1 production, requires a high degree of flexibility. To meet these requirements, manual production processes are frequently still used. This is especially applicable to the area of assembly. Data acquisition is a significant task in manual assembly due to volatile secondary activities and alternative handling operations. The process times to be recorded are also influenced both consciously and unconsciously by the employees. This paper describes an approach for the validation and interpretation of production data of manual assembly systems. Therefore, process data are analysed based on the use case of terminal strip assembly in the learning factory of the Chair of Production Systems at the Ruhr-University Bochum is presented. Here, the validation of the product data from 2021 is carried out by checking the data for normal distribution. This is followed by an analysis of the data with regard to the effects of spikes. Furthermore, the influences of a low data basis, different degrees of standardization and learning effects in the course of production are analysed. Finally, a discussion on the findings and further procedures will take place.