Syndromic surveillance is considered as one of the surveillance components for early warning of health-related events, as it allows detection of aberrations in health indicators before laboratory confirmation. "MoSS-Emergences 2" (MoSS-E2), a tool for veterinary syndromic surveillance, aggregates groups of similar clinical observations by hierarchical ascendant classification (HAC). In the present study, this HAC clustering process was evaluated using a reference set of data that, for the purpose of this evaluation, was a priori divided and defined as Bluetongue (BTV) positive cases (PC) on the one hand and BTV negative cases (NC) on the other hand. By comparing the clustering result of MoSS-E2 with the expected outcome, the sensitivity (the ability to cluster PC together) and specificity (the ability to exclude NC from PC) of the clustering process were determined for this set of data. The stability of the classes obtained with the clustering algorithm was evaluated by comparing the MoSS-E2 generated dendrogram (applying complete linkage) with dendrograms of STATA(®) software applying average and single linkage methods. To assess the systems' robustness, the parameters of the distance measure were adjusted according to different scenarios and obtained outcomes were compared to the expected outcome based on the a priori known labels. Rand indexes were calculated to measure similarity between clustering outcomes. The clustering algorithm in its default settings successfully segregated the reference BTV cases from the non-BTV cases, resulting in a sensitivity of 100.0% (95% CI: 89.0-100.0) and a specificity of 100.0% (95% CI: 80.0-100.0) for this set of data. The different linkage methods showed similar clustering results indicating stability of the classes (Rand indexes of respectively 0.77 for average and 0.75 for single linkage). The system proved to be robust when changing the parameters as the BTV cases remained together in meaningful clusters (Rand indexes between 0.72 and 1). The configurable MoSS-E2 system demonstrated its suitability to identify meaningful clusters of clinical syndromes.