Speech technology applications for major languages are becoming widely available, but for many other languages there is no commercial interest in developing speech technology. As the lack of technology and applications will threaten the existence of these languages, it is important to study how to create speech recognizers with minimal effort and low resources. As a test case, we have developed a Large Vocabulary Continuous Speech Recognizer for Northern Sámi, an Finno-Ugric language that has little resources for speech technology available. Using only limited audio data, 2.5 hours, and the Northern Sámi Wikipedia for the language model we achieved 7.6% Letter Error Rate (LER). With a language model based on a higher quality language corpus we achieved 4.2% LER. To put this in perspective we also trained systems in other, better-resourced, Finno-Ugric languages (Finnish and Estonian) with the same amount of data and compared those to state-of-the-art systems in those languages.