This paper proposes a new approach for edge detection using steerable filters and cellular neural networks (CNNs) where the former yields the local direction of dominant orientation and the latter, provides iterative filtering. For this purpose steerable filter coefficients are used in CNN as a $B$ template. The results are compared to the results where only CNN or steerable filters are used. As a result of this study, the performance of the system can be improved since iterative filtering property of CNN and the ability of steerable filters for edge detection are used.