BACKGROUND AND OBJECTIVES: People usually converse in real-life background noise. They experience more difficulty understanding speech in noise than in a quiet environment. The present study investigated how speech recognition in real-life background noise is affected by the type of noise, signal-to-noise ratio (SNR), and age. SUBJECTS AND METHODS: Eighteen young adults and fifteen middle-aged adults with normal hearing participated in the present study. Three types of noise [subway noise, vacuum noise, and multi-talker babble (MTB)] were presented via a loudspeaker at three SNRs of 5 dB, 0 dB, and -5 dB. Speech recognition was analyzed using the word recognition score. RESULTS: 1) Speech recognition in subway noise was the greatest in comparison to vacuum noise and MTB, 2) at the SNR of -5 dB, speech recognition was greater in subway noise than vacuum noise and in vacuum noise than MTB while at the SNRs of 0 and 5 dB, it was greater in subway noise than both vacuum noise and MTB and there was no difference between vacuum noise and MTB, 3) speech recognition decreased as the SNR decreased, and 4) young adults showed better speech recognition performance in all types of noises at all SNRs than middle-aged adults. CONCLUSIONS: Speech recognition in real-life background noise was affected by the type of noise, SNR, and age. The results suggest that the frequency distribution, amplitude fluctuation, informational masking, and cognition may be important underlying factors determining speech recognition performance in noise.