RNA viruses rapidly mutate, which can result in increased virulence, increased escape from vaccine protection, and false-negative detection results. Targeted detection methods have a limited ability to detect unknown viruses and often provide insufficient data to detect coinfections or identify antigenic variants. Random, deep sequencing is a method that can more fully detect and characterize RNA viruses and is often coupled with molecular techniques or culture methods for viral enrichment. We tested viral culture coupled with third-generation sequencing for the ability to detect and characterize RNA viruses. Cultures of bovine viral diarrhea virus, canine distemper virus (CDV), epizootic hemorrhagic disease virus, infectious bronchitis virus, 2 influenza A viruses, and porcine respiratory and reproductive syndrome virus were sequenced on the MinION platform using a random, reverse primer in a strand-switching reaction, coupled with PCR-based barcoding. Reads were taxonomically classified and used for reference-based sequence building using a stock personal computer. This method accurately detected and identified complete coding sequence genomes with a minimum of 20× coverage depth for all 7 viruses, including a sample containing 2 viruses. Each lineage-typing region had at least 26× coverage depth for all viruses. Furthermore, analyzing the CDV sample through a pipeline devoid of CDV reference sequences modeled the ability of this protocol to detect unknown viruses. Our results show the ability of this technique to detect and characterize dsRNA, negative- and positive-sense ssRNA, and nonsegmented and segmented RNA viruses.