In speech interfaces, it is often necessary to understand the overall auditory environment, not only recognizing what is being said, but also being aware of the location or actions surrounding the utterance. However, automatic speech recognition (ASR) becomes difficult when recognizing speech with environmental sounds. Standard solutions treat environmental sounds as noise, and remove them to improve ASR performance. On the other hand, most studies on environmental sounds construct classifiers for environmental sounds only, without interference of spoken utterances. But, in reality, such separate situations almost never exist. This study attempts to address the problem of simultaneous recognition of speech and environmental sounds. Particularly, we examine the possibility of using deep neural network (DNN) techniques to recognize speech and environmental sounds simultaneously, and improve the accuracy of both tasks under respective noisy conditions. First, we investigate DNN architectures including two parallel single-task DNNs, and a single multi-task DNN. However, we found direct multi-task learning of simultaneous speech and environmental recognition to be difficult. Therefore, we further propose a method that combines bottleneck features and sound-dependent i-vectors within this framework. Experimental evaluation results reveal that the utilizing bottleneck features and i-vectors as the input of DNNs can help to improve accuracy of each recognition task.