Purpose ‐ Because of the popularity of digital cameras, the number of personal photographs is increasing rapidly. In general, people manage their photos by date, subject, participants, etc. for future browsing and searching. However, it is difficult and/or takes time to retrieve desired photos from a large number of photographs based on the general personal photo management strategy. In this paper the authors aim to propose a systematic solution to effectively organising and browsing personal photos. Design/methodology/approach ‐ In their system the authors apply the concept of content-based image retrieval (CBIR) to automatically extract visual image features of personal photos. Then three well-known clustering techniques ‐ k-means, self-organising maps and fuzzy c-means ‐ are used to group personal photos. Finally, the clustering results are evaluated by human subjects in terms of retrieval effectiveness and efficiency. Findings ‐ Experimental results based on the dataset of 1,000 personal photos show that the k-means clustering method outperforms self-organising maps and fuzzy c-means. That is, 12 subjects out of 30 preferred the clustering results of k-means. In particular, most subjects agreed that larger numbers of clusters (e.g. 15 to 20) enabled more effective browsing of personal photos. For the efficiency evaluation, the clustering results using k-means allowed subjects to search for relevant images in the least amount of time. Originality/value ‐ CBIR is applied in many areas, but very few related works focus on personal photo browsing and retrieval. This paper examines the applicability of using CBIR and clustering techniques for browsing personal photos. In addition, the evaluation based on the effectiveness and efficiency strategies ensures the reliability of our findings.