Objective　The conditional hierarchical clustering for 1-dimensional(1-d) ordinal data was discussed.Methods　Because the individuals are ordered in 1-d,the conditional matrix was constructed with all elements in the second-diagonal are 1 and the others are 0.Distance matrix of individuals defined by some particular definition.Then the conditional-distance matrix was made for the hierarchical clustering by connecting the conditional matrix and distance matrix.This method was called 1-dimentsional conditional hierarchical clustering.An example was illustrated by this method and a Monte Carol study showed that method was feasible and robust.Results　Compared with the least-squares partition,this method is easy to understand,easy to practice and easy to compute.It also can give us a stable result.Conclusion　Because of the austere theory,the simple thought and the convenient application,it's a good method for the 1-d ordinal data.