Cystic fibrosis (CF) is the most common life-shortening inherited condition in people of European descent, affecting between ~80,000 people globally. CF pathogenesis is most prominent in the airways, where it causes the production of thick, tenacious mucus that provides ideal conditions for microbial pathogens to persist and thrive. Although metagenomics has shown promise with other clinical sample types (e.g. faeces), the CF microbiome has proven particularly challenging due to high (~99%) human DNA contamination, which overwhelms the microbial signal. Here, we used microbial metatranscriptomics (MMT) to examine the polymicrobial population of CF sputa from four Australian adult CF patients. MMT involves extracting total RNA, followed by removal of human mRNA and rRNA, leaving predominantly enriched bacterial mRNA and rRNA for sequencing. Unlike metagenomics, MMT provides an accurate snapshot of the ‘active’ polymicrobial population (i.e. no sequencing of residual ‘dead’ cells or reagent DNA contamination), it captures RNA viruses, it presents fewer ethical issues and greater human nucleic acid depletion efficiency, and it can theoretically identify gene expression differences conferring clinically relevant phenotypes for target species of interest (e.g. antimicrobial resistance caused by efflux pump upregulation). Among the four patients, MMT and 16S microbiomic sequencing yielded taxon assignments that were in broad agreement, although MMT had superior resolution; for example, MMT correctly identified miscalled Burkholderia sp. as Pseudomonas aeruginosa. Consistent with other studies, Gram-negative anaerobic bacteria were abundant, with Prevotella (mainly P. melaninogenica), and Veillonella spp. found in high abundance in all patients, and the pathogens Stenotrophomonas maltophilia and P. aeruginosa found in two and three patients, respectively. Taken together, we demonstrate the feasibility of MMT for unveiling the ‘active’ polymicrobial populations present in the CF airways. Although MMT is currently an immature method, it holds great promise for accurately characterising the composition, diversity, and function of polymicrobial infections. Future work is needed to assess the value of MMT for identifying antibiotic resistance and informing antibiotic treatment regimens in CF infections.