1. Popular current methods for quantifying variation in biological shape are wellsuited to analyses of isolated parts (e.g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple-part objects where each part has a different position, rotation or scale (e.g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple-part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. 2. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research. morphoBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple-part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple-part objects. 3. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. 4. RCPCA can be used to analyse two-or three-dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts. We use morphoBlocks on a small three-bone case study and provide a framework for applying this method to much larger studies investigating the ecological or evolutionary significance of multiple-part objects.