The vast majority of research on automatic chord transcription has been developed and tested on databases mainly focused on genres like pop and rock. Jazz is strongly based on improvisation, however, and the way harmony is interpreted is different from many other genres, causing state-of-the-art chord transcription systems to achieve poor performance. This article presents a computational system that transcribes chords from jazz recordings, addressing the specific challenges they present and considering their inherent musical aspects. Taking the raw audio and minor manually obtained inputs from the user, the system can jointly transcribe chords and detect the beat of a recording, allowing a lead sheet–like rendering as output. The analysis is implemented in two parts. First, all segments with a repeating chord progression (the chorus) are aligned based on their musical content using dynamic time warping. Second, the aligned segments are mixed and a convolutional recurrent neural network is used to simultaneously detect beats and transcribe chords. This automatic chord transcription system is trained and tested on jazz recordings only, and achieves better performance than other systems trained on larger databases that are not jazz specific. Additionally, it combines the beat-detection and chord transcription tasks, allowing the creation of a lead sheet–like representation that is easy to interpret by both researchers and musicians.