This chapter addresses pronunciation in second language (L2) learning, which ranges from phoneme-level pronunciation to conversation training. First, the definition of phonemes and their relationship with articulation are explained. Vowels and consonants are classified according to different dimensions. The concept of distinctive features is also described. These provide a basis to model and identify phoneme-level pronunciation errors. Suprasegmental features such as stress and rhythm are also addressed. Next, speech analysis methods are described. While formant analysis is effective for diagnosing the pronunciation of vowels, articulatory attribute detection is explored for comprehensive analysis of all phonemes. The chapter then introduces automatic speech recognition (ASR) technology to detect pronunciation errors. Settings of minimal pairs of words, prompted text, and free input can be designed. ASR models are also used for pronunciation grading. The goodness of pronunciation (GOP) score is computed for each phoneme and aggregated over all phonemes in the utterance. Nonnative speech modeling is crucial for effective L2 pronunciation learning.