Similarity between two Signals: cycle by cycle
so i'll throw the cross-correlation and/or matched-filter thing at you.  a little bit similar to Karthik but maybe a little more rigor.

so you have a reference signal, $y(t)$, and a measured signal $x(t)$.  suppose they have something in common and some differences and may one of the differences is a misalignment in time.  but if you investigate what these two signals have in common (or what they don't) for a variety of different offsets in time, that dissimilarity due to time misalignment can be made non-salient (or maybe you want to know the time offset).

so first consider measuring the dissimilarity (at a variety of different offsets or "lags") as:

$$ Q_{xy}(\tau) \triangleq \int\limits_{-\infty}^{+\infty} \left( x(t) - y(t+\tau) \right)^2 dt $$

so you're looking at how much $x(t)$ and $y(t+\tau)$ are different, for different lags, $\tau$, squaring that difference or "error" so that it's always positive (this is analytically easier than if you use the absolute value), adding up the total squared-difference over all of time and you have an overall measure in how dissimilar $x(t)$ and $y(t+\tau)$ are.  then picking the lag $\tau$ so that $Q_{xy}(\tau) \ge 0$ is minimum might be considered to be a measure on how dissimilar $x(t)$ and $y(t+\tau)$ are.

the smaller this measure (which is always non-negative), the more similar $x(t)$ and $y(t+\tau)$ are.  this is the Average Squared Difference Function (ASDF) and has a similar motivation in its definition as does the more well-known and historical AMDF function.

now the cool thing you get that you don't if it's AMDF is

$$ \begin{align}
Q_{xy}(\tau) &amp; = \int\limits_{-\infty}^{+\infty} \left( x(t) - y(t+\tau) \right)^2 \ dt  \\
&amp; = \int\limits_{-\infty}^{+\infty} x^2(t) + y^2(t+\tau) - 2x(t)y(t+\tau) \ dt \\
&amp; = \ \int\limits_{-\infty}^{+\infty} x^2(t) dt \ + \ \int\limits_{-\infty}^{+\infty}y^2(t+\tau) dt \ - \ 2\int\limits_{-\infty}^{+\infty}x(t)y(t+\tau) dt \\
&amp; = \ \int\limits_{-\infty}^{+\infty} x^2(t) dt \ + \ \int\limits_{-\infty}^{+\infty}y^2(t) dt \ - \ 2\int\limits_{-\infty}^{+\infty}x(t)y(t+\tau) dt \\
&amp; = \ E_x \ + \ E_y \ - \ 2\int\limits_{-\infty}^{+\infty}x(t)y(t+\tau) dt \\
\end{align} $$

$E_x$ and $E_y$ are the energies of $x(t)$ and $y(t)$ and not functions of $\tau$.  so $Q_{xy}(\tau)$ will be minimum with a $\tau$ that makes the integral on the right maximum.  we'll give that integral a name:

$$ R_{xy}(\tau) \triangleq \int\limits_{-\infty}^{+\infty}x(t)y(t+\tau) dt $$

and we see that 

$$  Q_{xy}(\tau) = E_x + E_y - 2  R_{xy}(\tau) $$

so so $Q_{xy}(\tau)$ is minimum when $R_{xy}(\tau)$ is maximum.  in fact, with a scaling constant of $2$ tossed in there $Q_{xy}(\tau)$ and $R_{xy}(\tau)$ are upside-down mirror images of each other.

we call $R_{xy}(\tau)$, the cross-correlation function of $x(t)$ and $y(t)$ and is the inner product (if we're pretending we're living in a Hilbert space) of $x(t)$ and $y(t+\tau)$.  if we're dealing with complex $x(t)$ and $y(t)$ (we're not, i hope) then $y(t)$ should be complex-conjugated.  where $R_{xy}(\tau)$ is maximum is the time offset that $x(t)$ and $y(t)$ are most similar to each other.  

the above can be rearranged:

$$ R_{xy}(\tau) = \frac{1}{2} \left( E_x + E_y - Q_{xy}(\tau) \right) $$

or 

$$ \frac{R_{xy}(\tau)}{\frac{1}{2}(E_x + E_y)} =  1 - \frac{Q_{xy}(\tau)}{E_x + E_y} $$

so the cross-correlation is getting normalized by the average energy of $x(t)$ and $y(t)$.  we're always subtracting a non-negative number from 1 so that normalized cross-correlation is never larger than 1

and pick the $\tau_0 \ne 0$ where $R_{xy}(\tau_0)&gt;R_{xy}(\tau)$ for all other $\tau$ and calculate that normalized cross-correlation and you have a measure of similarity with

$$ -1 \le \frac{R_{xy}(\tau_0)}{\frac{1}{2}(E_x + E_y)} \le 1 $$

if it's -1, they are identical but with opposite polarity.  if it's 1, they're totally similar (at that offset of $\tau_0$).