Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative characterisation of cell-type based on expression profiles. Due to the large variability in gene expression, assigning cells into groups based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 integrates many different clustering solutions through a consensus approach, thereby increasing its accuracy and robustness against noise. Tests on nine published datasets show that SC3 outperforms existing methods, yet SC3 remains scalable for large datasets, as shown by the analysis of a dataset containing ~45,000 cells. To enhance the accessibility to users with limited bioinformatics expertise, SC3 features an interactive graphical implementation, which aids the biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. Finally, we apply SC3 to identify different subclones of neoplastic cells in data collected from patients.