This work presents a robust environmental sound recognition system for home automation. Specific home automation services can be activated based on identified sound classes. Additionally, when the sound category is human speech, such speech can be recognized for detecting human intentions as in conventional research on home automation. To attain this ambitious goal, this study uses two key techniques: signal-to-noise ratio-aware subspace-based signal enhancement and sound recognition with independent component analysis mel-frequency cepstral coefficients and a frame-based multiclass support vector machines, respectively. Simulations and an experiment in a real-world environment are given to illustrate the performance of the proposed robust sound recognition system.