Monitoring indexes are significant for real-time monitoring of dam performance in ensuring safe and normal operation. Traditional methods for establishing monitoring indexes are mostly focused on single point displacements, and rational monitoring indexes based on multi-point displacements are rare. This study establishes monitoring indexes based on correlation and discreteness of multi-point displacements. The proposed method is applicable when several monitoring points show strong correlation. In this study, principal component analysis (PCA) was introduced for preprocessing the observations of multi-point displacements. Correlation and discreteness of multi-point displacements were extracted and constructed. The correlation and discreteness parts described the integral and local variance of the displacement field. On this basis, the annual maximum values of the correlation and discreteness parts were selected and their probability density functions (PDF) could be generated by employing the principle of maximum entropy. PDF was constructed using maximum entropy method and was least subjective because it barely provided the moment information of the observations. The multi-point monitoring indexes were then determined by the typical low probability method based on the obtained PDFs. Finally, the proposed method was analyzed using a practical engineering and was verified in terms of its feasibility.