Sutton's TD(λ) method aims to provide a representation of the cost function in an absorbing Markov chain with transition costs. A simple example is given where the representation obtained depends on λ. For λ = 1 the representation is optimal with respect to a least-squares error criterion, but as λ decreases toward 0 the representation becomes progressively worse and, in some cases, very poor. The example suggests a need to understand better the circumstances under which TD(0) and Q-learning obtain satisfactory neural network-based compact representations of the cost function. A variation of TD(0) is also given, which performs better on the example.