We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low-pressure systems (LPSs) in a given climate dataset. We use the state-of-the-art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly-found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point (node) and designates four different types of high-impact LPS tracks, including tropical cyclone (TC) tracks, Monsoon System (MS) tracks, subtropical tropical-like cyclone (STLC) tracks, and polar low (PL) tracks. The classification process involves disentangling high-altitude and drier LPSs, differentiating tropical and non-tropical LPSs using novel criteria, and optimizing for the detection of the four types of high-impact LPS. We compare our labels to those in the International Best Track Archive for Climate Stewardship (IBTrACS) and find that they are in good agreement. TC detection using SyCLoPS produces better tropical cyclone detection skill compared to the previous algorithms. Finally, we demonstrate that the output of SyCLoPS is valuable for investigating various aspects of LPSs, such as the evolution of a single LPS track, patterns and trends in LPS activities, and precipitation or wind influence associated with a particular LPS class. We expect that these feature will enhance studies on tropical cyclones and tropical-like cyclones across the climate science community.