Workflows for microbiome community profiling by high-throughput sequencing are prone to sample mix-ups and cross-contamination due to the complexity of the procedures and large number of samples typically analyzed in parallel. We employed synthetic 16S rRNA gene spike-in controls to establish a method for tracking of sample identity and detection of cross-contamination in microbiome community profiling assays based on 16S rRNA gene amplicon sequencing (16S-seq). Results demonstrated that combinatorial sample tracking mixes (STMs) can be reliably resolved by Illumina sequencing and faithfully represent their sample of origin. In a single-blinded experiment, addition of STMs at low levels was shown to be sufficient to unambiguously identify and resolve swapped samples. Using artificial admixtures of individually SMT-tagged samples, we further established the ability to detect and quantify cross-contamination down to a level of approximately 1%. The utility of our technique was underscored through detection of an unplanned case of cross-contamination that occurred during this study. By enabling detection of sample mix-ups and cross-contamination throughout 16S-seq workflows, the present technique thus assures provenance of sequence data on a per-sample basis. The method can be readily implemented in standard 16S-seq workflows and its routine application is expected to enhance the reliability of 16S-seq data.