In the context of structural condition assessment, transfer learning methods overcome some of the difficulties associated with the paucity of information on the actual structural condition of a target structure. This study aims to associate the building’s response with a population of nominally identical buildings for which a form is derived from existing empirical models relating certain basic characteristics (e.g., structure height) with the fundamental resonance period. This paper presents a structural condition classification based on the measured resonance period of the target structure, presented as the Build’Health™ solution. First, damage thresholds are defined by the shift of the fundamental period, which is considered to be a damage-sensitive characteristic for a given building population form, derived from almost thirty published references. The implicit period variation due to certain weather conditions is also included. Multinomial logistic regression and Gaussian mixture models are then used to classify damage according to the performance levels used in earthquake engineering (i.e., Operational Condition, Immediate Occupancy, Life Safety and Collapse Prevention). A performance-based probabilistic framework using a traffic-light system (green-orange-red classification) is finally used to classify structural condition. The method is tested and validated on several buildings surveyed after weak to strong earthquakes with different structural conditions. We show the complementarity of combining transfer learning, which gives the actual state of the target specimen with respect to a nominally identical population form of buildings, with multinomial logistic regressions and Gaussian mixture models for operational condition-based decision-making defined by the measured resonance period. This manuscript is the second in a series aimed at developing the Build’Health™ operational method for assessing the condition of real buildings (Part I on damage detection using transfer learning and Part II on classification using Gaussian mixture models and multinomial logistic regressions) based on basic building information. •Structural condition classification based on the measured resonance period of the target structure.•Damage thresholds defined by the shift of the fundamental period found in references, for a given building population form.•Multinomial logistic regression and Gaussian mixture models used to classify damage according to the performance levels.•Performance-based probabilistic framework using a traffic-light system used to classify structural condition.•Test and validation on several buildings with different structural conditions.