This study was conducted with the aim of morphometrical characterization and structural indices to assess type and function of indigenous goats reared in two production system in Sidama Zone, Southern Ethiopia. The study encompassed both qualitative and quantitative parameters which were taken from 780 head of adult goats of both sexes from age 1PPI to 4PPI. Age was estimated by dentition. The qualitative traits were assessed visually and recorded while; quantitative traits were assessed using self-devised equipment. Thirteen morphometrical measurements were taken per goat and from which also indices were calculated. Data collected by visual observation and linear measurements were analyzed by Statistical Package for Social Sciences (SPSS version, 20) and the value was considered significance at P<0.05 and P<0.01. The qualitative data were assessed by chi–square test. The observed coat color patterns in studied area for both sexes across the districts were plain. Most frequently observed coat color type from sampled goats at Aroresa district were brown and black while, most frequently observed coat color type at Lokabaya district were white. The results of morphometrical measurements indicated that Does age group of 1 and 2 and 3 and 4 reared at Aroresa district had higher value for most measured traits. Bucks age group 1 and 2 reared at Lokabaya district had higher value for their body length, chest girth, rump length, horn length, body weight, rump height and chest depth except rump width, face length and head width which is higher for the Buck in age group 1 and 2 reared at Aroresa district. The Bucks reared at Lokabaya district had higher values for their neck circumference and body weight while, the Bucks reared at Aroresa district had higher value for their head width. In generally, most morphometric traits were different between the goats reared in the studied districts and these variations indicate the possibilities for selection between the goats in the areas. The regression was done to regress body weight from linear body measurements for different age and sex groups separately. As a result from multiple regressions, chest girth, body length, chest depth and neck circumference were the best fitted models to predict body weight for different age and sex groups separately.