With over 5000 exoplanets currently detected, there is a need for a primary classification method to prioritise candidates for biosignature observations. Here, we develop a classification method to categorise rocky exoplanets based on their closest solar system analogue using available data of observed stellar and planetary features, masses, and radii, to model non-thermal atmospheric escape, thermal atmospheric escape, and stellar irradiation boundaries. Applying this classification method to the 720 rocky exoplanets in our sample with uncertainties in planetary masses, radii, stellar temperatures, and fluxes propagated via a Monte Carlo model indicates that 22%+/-8% are Mercury analogues, 39%+/-4% are Mars analogues, 11%+/-1% are Venus analogues, 2%+/-1% are Earth analogues, and 26%+/-12% are without a known planetary counterpart in our solar system. Extrapolating to conditions on LHS 3844b and GJ 1252b, our classification method gives results reasonably consistent with the observations by Kreidberg et al. (2019Natur.573...87K) and Crossfield et al. (2022ApJ...937L..17C), respectively. Subsequently, to demonstrate the functionality of this classification method, we plot our catalogued sample of exoplanets on an adjusted surface pressure versus temperature phase diagram, presenting more realistic estimates of the potential surface phases (gas, liquid or ice). Our new classification method could help target selection for future exoplanet characterisation missions.