Abstract: The peak-valley annual ring recognition algorithm only uses the feature of the difference between the peak value and valley value for annual ring recognition, so the algorithm has a high rate of false positives and false negatives. To enhance the accuracy of micro-drill resistance ring recognition, the Back Propagation-Artificial Neural Network (BP-ANN) algorithm was employed. Firstly, the peak-valley ring recognition algorithm was utilized to identify the effective peaks. Secondly, characteristics such as peak resistance value, resistance difference between the peak and adjacent valleys, distance between the peak and adjacent valleys, and distance between front and back valleys were employed to describe these peaks. Based on the analysis of resistance graphs and disk images, the effective peaks were classified accordingly. If the peak was an annual ring signal, it was marked as "1"; otherwise, it was marked as "0". Finally, an effective peak classification model was constructed using BP-ANN algorithm. Compared to the peak valley annual ring recognition algorithm, the BP-ANN model improved the accuracy by 1.26 percentage points and reduced false positives and false negatives by 1.06 and 1.38 percentage points, respectively. The results indicated that the BP-ANN model based on multiple peak features was feasible for identifying annual rings. Compared with the traditional peak-valley annual ring recognition algorithms, the proposed method can effectively improve the accuracy of annual ring recognition and reduce the misjudgment and omission rates of annual rings.