Falls are a prominent problem facing older adults and a common cause of hospitalized injuries. Accurate falls-risk assessment and classification of falls-risk levels will provide useful information for the prevention of future falls. This study presents a triaxial accelerometer (TA) based two-class classifier, which discriminates between multiple fallers and non-multiple fallers, using a directed-routine (DR) movement test. One-hundred-and-twenty-six features were extracted from the accelerometry signals, recorded during the DR tests using a waist mounted TA, from 68 subjects. A linear multiple regression model was employed to map a subset of these features to an estimate of the number of previous falls experienced in the preceding twelve months. A simple threshold is applied to this estimated number of falls to create a basic linear discriminant classifier to separate multiple from non-multiple fallers. The system attained an accuracy of 71% in classifying the exact number of falls experienced in the last 12 months and 97% in identifying multiple fallers.