Earth has been a traditional building material to construct structures in many different continents. In particular, adobe buildings are widely diffused in South America, and in Peru where form part of the cultural identity of the nation. Nowadays, the knowledge of existing adobe buildings is far from a complete understanding of the constructive system and a structural health monitoring (SHM) can quantify and reduce uncertainties regarding their structural performance without causing damage to the buildings. In this process, the implementation of automatic tools for feature extraction of modal parameters is desirable. In particular, the automation is important because, during a long-term monitoring, a huge amount of data is recorded and the direct check of the data of the user is not possible. The present work is focused on the development of an automated procedure for managing the results obtained from the parametric identification method, in particular from the Data-Driven Stochastic Subspace Identification method, which requires an automatic interpretation of stabilization diagrams. The work presents a fully automated modal identification methodology based on the following steps: (i) digital signal pre-processing of the recorded data; (ii) modal parameter identification using models with varying dimensions; (iii) automatic analysis of the stabilization diagram with the application of soft and hard validation criteria and the use of hierarchical clustering approach to eliminate the spurious modes; and (iv) automatic choice of the most representative values of the estimated parameters of each clustered mode: natural frequency, damping and mode shape. The developed algorithm was firstly tested with an inverted steel pendulum to check the accuracy and sensitivity, and subsequently, an earthen wall built in PUCP Structure Laboratory was analysed to determine its dynamic behaviour. The developed algorithm shows high percentages of detected frequencies and high sensitivity to the environmental and structural changes. (c) 2017 The Authors. Published by Elsevier Ltd.