Smart spaces are characterized by their ability to capture a holistic picture of their contextual situation. This often includes the detection of the operative states of electrical appliances, which in turn allows for the recognition of user activities and intentions. For electrical appliances with largely different power consumption characteristics, their types and operational times can be easily inferred from data collected at a single metering point (typically, a smart meter). However, a disambiguation between consumers of the same type and model, yet located in different areas of a smart building, is not possible this way. Likewise, small consumers (e.g., wall chargers) are often indiscernible from measurement noise and spurious power consumption events of other appliances. As a consequence thereof, we investigate how additional sensing modalities, i.e., data beyond electrical signals, can be leveraged to improve the appliance detection accuracy. Through a set of practical experiments, recording ambient influences in eight dimensions and testing their effects on 21 appliance types, we evaluate the importance of such added features in the context of appliance recognition. Our results show that electrical power measurements already yield a high appliance recognition accuracy, yet further accuracy improvements are possible when considering ambient parameters as well.