This paper shows the feasibility of identifying liquids by shining ultra-wideband (UWB) wireless signals through them. The core opportunity arises from the fact that wireless signals experience distinct slow-down and attenuation when passing through a liquid, manifesting in the phase, strength, and propagation delay of the outgoing signal. While this intuition is simple, building a robust system entails numerous challenges, including (1) pico-second scale time of flight estimation, (2) coping with integer ambiguity due to phase wraps, (3) pollution from hardware noise and multipath, and (4) compensating for the liquid-container’s impact on the measurements. We address these challenges through multiple stages of signal processing without relying on any feature extraction or machine learning. Instead, we model the behavior of radio signals inside liquids (using principles of physics), and estimate the liquid’s permittivity, which in turn identifies the liquid. Experiments across 33 different liquids (spread over the whole permittivity spectrum) show median permittivity error of 9%. This implies that coke can be discriminated from diet coke or pepsi, whole milk from 2% milk, and distilled water from saline water. Our end system, LiquID, is cheap, non-invasive, and amenable to real-world applications.