Mycobacterium tuberculosis (Mtb) infections cause tuberculosis (TB), an infectious disease which causes ∼1.5 million deaths annually. The ability of this pathogen to evade, escape and encounter immune surveillance is fueled by its adaptability. Thus, Mtb induces a transition in its transcriptome in response to environmental changes. Global transcriptome profiling has been key to our understanding of how Mtb responds to the different stress conditions it faces during its life cycle. While this was initially achieved using microarray technology, RNAseq is now widely employed. It is important to understand the correlation between the large amount of microarray based transcriptome data, which continues to shape our understanding of Mtb stress networks, and newer data being generated using RNAseq. We assessed how well the two platforms correlate using three well-defined stress conditions: diamide, hypoxia, and re-aeration. The data used here was generated by different individuals over time using distinct samples, providing a stringent test of platform correlation. While correlation between microarrays and sequencing was high upon diamide treatment, which causes a rapid reprogramming of the transcriptome, RNAseq allowed a better definition of the hypoxic response, characterized by subtle changes in the magnitude of gene-expression. RNAseq also allows for the best cross-platform reproducibility.