Financial ratios are often used in cluster analysis to classify firms according to the similarity of their financial structures. Besides the dependence of distances on ratio choice, ratios themselves have a number of serious problems when subject to a cluster analysis such as skewed distributions, outliers, and redundancy. Some solutions to overcome those drawbacks have been proposed in the literature, but have proven problematic. In this work we put forward an alternative financial statement analysis method for classifying firms which aims at solving the above mentioned shortcomings and draws from compositional data analysis. The method is based on the use of existent clustering methods with standard software on transformed data by means of the so-called isometric logarithms of ratios. The method saves analysis steps (outlier treatment and data reduction) while defining distances among firms in a meaningful way which does not depend on the particular ratios selected. We show examples of application to two different industries and compare the results with those obtained from standard ratios.